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Mountain ecosystems are significantly affected by climate change. However, due to slow vegetation growth in mountain ecosystems, climate-induced vegetation shifts are difficult to detect with low-definition remote sensing images. We used high-definition remote sensing data to identify responses to climate change in a typical Picea crassifolia Kom. forest in the Qilian Mountains, China, from 1968 to 2017. We found that: (1) Picea crassifolia Kom. forests were distributed in small patches or strips on shaded and partly shaded slopes at altitudes of 2700&ndash, 3250 m, (2) the number, area, and concentration of forest patches have been increasing from 1968 to 2017 in relatively flat and partly sunny areas, but the rate of area increase and ascend of the tree line slowed after 2008, and (3) the establishment of plantation forests may be one of the reasons for the changes. The scale of detected change in Picea crassifolia Kom.forest was about or slightly below 30 m, indicating that monitoring with high-resolution remote sensing data will improve detectability and accuracy.

It is important for integrated forest-water management to develop a better understanding of the variation of tree transpiration among different canopy layers in the forests and its response to soil moisture and weather conditions. The results will provide insights into water consumption by trees occupying different social positions of the forests. In the present study, an experiment was conducted in the Qilian Mountains, northwest China, and 13 trees, i.e., 4&ndash, 5 trees from each one of dominant (the relative tree height (HR) &gt, 1.65), subdominant (1.25 &lt, HR &le, 1.65) and intermediate-suppressed (HR &le, 1.25) layers) were chosen as sample trees in a pure Qinghai spruce (Picea crassifolia Kom.) forest stand. The sap flux density of sample trees, soil moisture of main root zone (0 to 60 cm) and meteorological conditions in open field were observed simultaneously from July to October of 2015 and 2016. The results showed that (1) The mean daily stand transpiration for the study period in 2015 and 2016 (July&ndash, October), was 0.408 and 0.313 mm&middot, day&minus, 1, and the cumulative stand transpiration was 54.84 and 40.97 mm, accounting for 24.14% (227.2 mm) and 16.39% (249.9 mm) of the total precipitation over the same periods, respectively. (2) The transpiration varied greatly among canopy layers, and the transpiration of the dominant and codominant layers was the main contributors to the stand transpiration, contributing 86.05% and 81.28% of the stand transpiration, respectively, in 2015 and 2016. (3) The stand transpiration was strongly affected by potential evapotranspiration (PET) and volumetric soil moisture (VSM). However, the transpiration of trees from the dominant and codominant layers was more sensitive to PET changes and that from the intermediate-suppressed layer was more susceptible to soil drought. This implied that in dry period, such as in drought events, the dominant and codominant trees would transpire more water, while the intermediate-suppressed trees almost stopped transpiration. These remind us that the canopy structure was the essential factor affecting single-tree and forest transpiration in the dryland areas.

Leaf stoichiometry of plants can respond to variation in environments such as elevation ranging from low to high and success in establishing itself in a given montane ecosystem. An evaluation of the leaf stoichiometry of Qinghai Spruce (Picea crassifolia Kom.) growing at different elevations (2400 m, 2600 m, 2800 m, 3000 m, and 3200 m) in eastern China’s Qilian Mountains, showed that leaf carbon (LC) and leaf phosphorus (LP) were similar among elevations, with ranges of 502.76–518.02 g·kg−1, and 1.00–1.43 g·kg−1, respectively. Leaf nitrogen (LN) varied with changes of elevation, with a maxima of 12.82 g·kg−1 at 2600 m and a minima of 10.74 g·kg−1 at 2800 m. The LC:LN under 2400 m and 2600 m was lower than that under other elevations, while LC:LP and LN:LP were not different among these elevations. Except for LN and LC:LN, P. crassifolia’s other leaf stoichiometries remained relatively stable across elevations, partly supporting the homeostasis hypothesis. Variations in leaf stoichiometry across elevations were mainly linked to mean annual precipitation, mean annual temperature, soil pH, and the soil organic C to soil total N ratio. P. crassifolia growth within the study area was more susceptible to P limitation.

Tree growth strongly responds to climate change, especially in semiarid mountainous areas. In recent decades, China has experienced dramatic climate warming, however, after 2000 the warming trend substantially slowed (indicative of a warming hiatus) in the semiarid areas of China. The responses of tree growth in respect to elevation during this warming hiatus are poorly understood. Here, we present the responses of Qinghai spruce (Picea crassifolia Kom.) growth to warming using a stand-total sampling strategy along an elevational gradient spanning seven plots in the Qilian Mountains. The results indicate that tree growth experienced a decreasing trend from 1980 to 2000 at all elevations, and the decreasing trend slowed with increasing elevation (i.e., a downward trend from &minus, 10.73 mm2 year&minus, 1 of the basal area increment (BAI) at 2800 m to &minus, 3.48 mm2 year&minus, 1 of BAI at 3300 m), with an overall standard deviation (STD) of 2.48 mm2 year&minus, 1. However, this trend reversed to an increasing trend after 2000, and the increasing trends at the low (2550&ndash, 2900 m, 0.27&ndash, 5.07 mm2 year&minus, 1 of BAI, p &gt, 0.23) and middle (3000&ndash, 3180 m, 2.08&ndash, 2.46 mm2 year&minus, 0.2) elevations were much weaker than at high elevations (3300 m, 23.56 mm2 year&minus, 1 of BAI, p &lt, 0.01). From 2000&ndash, 2013, the difference in tree growth with elevation was much greater than in other sub-periods, with an overall STD of 7.69 mm2 year&minus, 1. The stronger drought conditions caused by dramatic climate warming dominated the decreased tree growth during 1980&ndash, 2000, and the water deficit in the 2550&ndash, 3180 m range was stronger than at 3300 m, which explained the serious negative trend in tree growth at low and middle elevations. After 2000, the warming hiatus was accompanied by increases in precipitation, which formed a wetting&ndash, warming climate. Although moisture availability was still a dominant limiting factor of tree growth, the relieved drought pressure might be the main reason for the recent recovery in the tree growth at middle and low elevations. Moreover, the increasing temperature significantly promoted tree growth at 3300 m, with a correlation coefficient between the temperature and BAI of 0.77 (p &lt, 0.01). Our results implied that climate change drove different growth patterns at different elevations, which sheds light into forest management under the estimated future climate warming: those trees in low and middle elevations should be paid more attention with respect to maintaining tree growth, while high elevations could be a more suitable habitat for this species.

Leaf stable isotope compositions (&delta, 13C and &delta, 15N) are influenced by various abiotic and biotic factors. Qinghai spruce (Picea crassifolia Kom.) as one of the dominant tree species in Qilian Mountains plays a key role in the ecological stability of arid region in the northwest of China. However, our knowledge of the relative importance of multiple factors on leaf &delta, 15N remains incomplete. In this work, we investigated the relationships of &delta, 15N to leaf age, tree age and leaf nutrients to examine the patterns and controls of leaf &delta, 15N variation of Picea crassifolia. Results showed that 13C and 15N of current-year leaves were more enriched than older ones at each tree age level. There was no significant difference in leaf &delta, 13C values among trees of different ages, while juvenile trees (&lt, 50 years old) were 15N depleted compared to middle-aged trees (50&ndash, 100 years old) at each leaf age level except for 1-year-old leaves. Meanwhile, relative importance analysis has demonstrated that leaf age was one of the most important indicators for leaf &delta, 15N. Moreover, leaf N concentrations played a dominant role in the variations of &delta, 15N. Above all, these results provide valuable information on the eco-physiological responses of P. crassifolia in arid and semi-arid regions.

It is important to explore the responses of radial tree growth in different regions to understand growth patterns and to enhance forest management and protection with climate change. We constructed tree ring width chronologies of Picea crassifolia from different regions of the Qilian Mountains of northwest China. We used Pearson correlation and moving correlation to analyze the main climate factors limiting radial growth of trees and the temporal stability of the growth–climate relationship, while spatial correlation is the result of further testing the first two terms in space. The conclusions were as follows: (1) Radial growth had different trends, showing an increasing followed by a decreasing trend in the central region, a continuously increasing trend in the eastern region, and a gradually decreasing trend in the isolated mountain. (2) Radial tree growth in the central region and isolated mountains was constrained by drought stress, and tree growth in the central region was significantly negatively correlated with growing season temperature. Isolated mountains showed a significant negative correlation with mean minimum of growing season and a significant positive correlation with total precipitation. (3) Temporal dynamic responses of radial growth in the central region to the temperatures and SPEI (the standardized precipitation evapotranspiration index) in the growing season were unstable, the isolated mountains to total precipitation was unstable, and that to SPEI was stable. The results of this study suggest that scientific management and maintenance plans of the forest ecosystem should be developed according to the response and growth patterns of the Qinghai spruce to climate change in different regions of the Qilian Mountains.

Global climate change is likely to change precipitation patterns with consequences for tree water use and growth in semi-arid areas. However, little is known about the effects of variability in precipitation on growth- and water-related physiological processes of native trees in dry areas of northwestern China. In this study, sap flow and stem radial variability in four Qinghai spruce trees (Picea crassifolia) were monitored in the Qilian Mountains, China. Tree water deficit (ΔW) and basal area increment (BAI) were calculated using stem radial variation; water-use efficiency (WUE) was then estimated as the ratio of BAI and sap flow (Jt). The results showed that sap flow density (Js) increased logarithmically with increasing ΔW when ΔW < 50 μm, and then gradually stabilized. Multiple factor generalized additive models (GAM) showed that Js was closely related to all measured environmental variables except for daily mean temperature and relative air humidity. ΔW was related to the minimum daily temperature and soil water content. WUE exhibited higher values in early July. Low WUE was observed under conditions of prolonged dry weather, but it quickly increased during rainy days. WUE decreased after precipitation events due to high transpiration. We concluded that, in these semi-arid areas, precipitation is the most important controlling factor in tree growth and transpiration.

Light detection and ranging (LiDAR) has been widely used to estimate forest biomass. In this study, we aim to further explore this capability by correlating horizontal and vertical distribution of LiDAR data with components of biomass in a Picea crassifolia forest. Airborne small footprint full-waveform data were decomposed to acquire higher density point clouds. We calculated LiDAR metrics at the tree level and subplot level and correlated them to biomass components, including branch biomass (BB), leaf biomass (LB) and above-ground biomass (AGB), respectively. A new metric (Horizcv) describing the horizontal distribution of point clouds was proposed. This metric was found to be highly correlated with canopy biomass (BB and LB) at the tree level and subplot level. Correlation between AGB and Horizcv at the subplot level is much lower than that at tree level. AGB for subplot is highly correlated with the mean height metric (Hmean), canopy cover index (CCI) and the product of them. On the other hand, the relationship between the vertical distribution of LiDAR point and biomass was explored by developing two types of vertical profiles, including LiDAR distribution profiles and a biomass profile. Good relationships were found between these two types of vertical profiles and assessed by Pearson’s correlation coefficient (R) and the area of overlap index (AOI). These good correlations possess potential in predicting the vertical distribution of canopy biomass. Overall, it is concluded that not only the vertical, but also the horizontal distribution of LiDAR points should be taken into account in estimating components of biomass by LiDAR.

Rainfall interception loss of forest is an important component of water balance in a forested ecosystem. The Gash analytical model has been widely used to estimate the forest interception loss at field scale. In this study, we proposed a simple model to estimate rainfall interception loss of heterogeneous forest at regional scale with several reasonable assumptions using remote sensing observations. The model is a modified Gash analytical model using easily measured parameters of forest structure from satellite data and extends the original Gash model from point-scale to the regional scale. Preliminary results, using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) products, field measured rainfall data, and meteorological data of the Automatic Weather Station (AWS) over a picea crassifolia forest in the upper reaches of the Heihe River Basin in northwestern China, showed reasonable accuracy in estimating rainfall interception loss at both the Dayekou experimental site (R2 = 0.91, RMSE = 0.34 mm∙d −1) and the Pailugou experimental site (R2 = 0.82, RMSE = 0.6 mm∙d −1), compared with ground measurements based on per unit area of forest. The interception loss map of the study area was shown to be strongly heterogeneous. The modified model has robust physics and is insensitive to the input parameters, according to the sensitivity analysis using numerical simulations. The modified model appears to be stable and easy to be applied for operational estimation of interception loss over large areas.

Dryland montane forests conserve water for people living in the fluvial plains. The fate of these forests under climate warming is strongly affected by local environmental factors. The question remains of how internal factors contribute to climate change impacts on forest growth in these regions. Here, we investigated tree ring records for similar-aged stand-grown trees and their neighboring open-grown trees at elevation in a dryland montane forest (Picea crassifolia Kom.) in northwestern China. The growth rate of open-grown trees is much higher than their neighboring stand-grown trees across the entire elevation gradient, and the lower the altitude, the greater the difference. Open-grown trees at different elevations showed similar growth patterns, as tree growth at all sites was accelerated over time. In contrast, growth patterns of stand-grown trees were divergent at different altitudes, as growth at high elevations (3100&ndash, 3300 m a.s.l.) was accelerated, whereas growth at low elevations (2700&ndash, 2900 m a.s.l.) became stable after the year 1990. Analysis of growth&ndash, climate relationships indicated that warming promoted open-grown tree growth across the entire altitude gradient, and also stand-grown tree growth at high elevations, but negatively affected the growth of stand-grown trees at low elevations. Water scarcity can be exacerbated by competition within forests, inhibiting the warming-induced benefits on tree growth. Moving window correlation analysis suggested the negative effect of warming on tree growth at low elevations was diminished after the late 1990s, as the drought stress was alleviated. Our research shows the divergent growth responses to warming of stand-grown and open-grown trees along elevation. It reveals effects of internal factors in determining tree growth response to warming and holds the potential to aid forest management and ecosystem models in responding to climate change.

This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R2 values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg·ha−1, respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R2 values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg·ha−1, respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R2 value of 0.356, an RMSE of 3.691 Mg·ha−1, and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries.

Picea crassifolia Komarov (Qinghai spruce) is an endemic tree species in China and is widespread in the Qilian Mountains, in northwestern China. High temporal resolution changes of Qinghai spruce tree stem growth remain poorly investigated and the relationships between the species growth and climate are still not completely understood. In this study, we assessed the daily and seasonal stem radial variations, and analyzed the relationships between stem radial increment of Qinghai spruce and environmental factors during the main growing period (June–August). We have found that the stem radial variations of Qinghai spruce can be divided into three phases according to the air temperature and that Qinghai spruce has two diurnal cycle patterns. The main growing period of Qinghai spruce is 30 May–31 August according to micro-core measurements, in conformity with the daily mean air temperature keeping above 5 °C. Precipitation and relative humidity have positive effects on the growth of Qinghai spruce, and we develop a multiple linear regression model that can explain 63% of the stem radial increment over the main growing period.

Qinghai spruce forests play a key role in water conservation in the dry region of northwest China. So, it is necessary to understand the impacts of climate change on the species to implement adaptation strategies. Based on the four-emission scenario (i.e., RCP2.6 (Representative Concentration Pathway), RCP4.5, RCP6.0 and RCP8.5) set by the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report, in the study, we predicted the potential distribution of Qinghai spruce (Picea crassifolia Kom.) under current and future scenarios using a maximum entropy (Maxent) model. Seven variables, selected from 22 variables according to correlation analysis combining with their contribution rates to the distribution, are used to simulate the potential distribution of the species under current and future scenarios. Simulated results are validated by area under the operating characteristic curve (AUC). Results demonstrate that elevation, mean temperature of wettest quarter, annual mean temperature, and mean diurnal range are more important in dominating the potential distribution of Qinghai spruce. Ratios of the suitable area to the total study area are 34.3% in current climate condition, 34% in RCP2.6, 33.9% in RCP4.5, 33.8% in RCP6.0, and 30.5% in RCP8.5, respectively. The warmer the climate condition is, the more area of higher suitable classification is changed to that of lower suitable classification. The ratios of real distribution area in simulated unsuitable class to the real distribution area change from 4.3% (60.7 km2) in the current climate to 13% (185 km2) in RCP8.5, suggesting that the real distribution area may decrease in the future. We conclude that there is a negative effect of climate change on the distribution of Qinghai spruce forest. The result can help decision-makers to draft adaptation countermeasures based on climate change.

The response of radial growth to climate and the climate sensitivity of tree growth at different ages in different drought conditions are essential for predicting forest dynamics and making correct forest management policies. In this study, we analyzed the growth responsiveness of Picea crassifolia Kom. to climate and explored the relationship between age and climate sensitivity of radial growth at the individual tree scale in the wetter eastern area and drier western area of the Qilian Mountains. Pearson correlation coefficients were calculated between the chronology of each tree and climatic factors to examine the climate-growth relationships. Linear fitting, quadratic polynomial fitting and exponential fitting were used to test the relationships between age and mean sensitivity, standard deviation and radial growth’s response to climate. Trees in the wetter eastern area showed a weaker response to climate than those in the drier western area and were significantly correlated with precipitation and mean temperature in the previous and current mid-late summer. Trees in the drier western area were mainly limited by precipitation of the previous August, the current May and June, as well as limited by temperature in the previous and current early-middle summer. In the wetter area, the younger trees were more sensitive to both precipitation and temperature than the older trees. In the drier area, younger/older trees showed a stronger sensitivity to precipitation in the current August and September/May, whereas trees 120–140 years old showed a stronger correlation with temperature factors in the summer. It was determined that mature trees in the drier area were more strongly influenced by the climate, especially in the context of increasing temperature. These trees should be paid special attention in forest management.

Carbon storage of mountain forests is vulnerable to climate change but the changes in carbon flux through time are poorly understood. Moreover, the relative contributions to carbon flux of drivers such as climate and atmospheric CO2 still have significant uncertainties. We used the dynamic model LPJ-GUESS with climate data from twelve meteorological stations in the Qilian Mountains, China to simulate changes in carbon mass of a montane boreal forest, and the influence of temperature, precipitation, and CO2 concentration from 1964 to 2013 on carbon flux. The results showed that the carbon mass has increased 1.202 kg/m2 from 1964 to 2013, and net primary productivity (NPP) ranged from 0.997 to 1.122 kg/m2/year. We concluded that the highest carbon mass proportion for this montane boreal forest was at altitudes 2700–3100 m (proportion of ecosystem carbon was between 93–97%), with maximum carbon density observed at 2700–2900 m. In the last 50 years, the increase in precipitation and in CO2 concentration is expected to increase carbon mass and NPP of Picea crassifolia Kom. (Pinaceae) (Qinghai spruce). The effect of temperature on NPP was positive but that on carbon mass was not clear. The increase in CO2 concentration over the past 50 years was a major contributor to the increase in carbon storage, and drought was the foremost limiting factor in carbon storage capacity of this montane boreal forest. Picea crassifolia forest was vulnerable to climate change. Further studies need to focus on the impact of extreme weather, especially drought, on carbon storage in Picea crassifolia forests.