Vegetative precipitation-use efficiency (PUE) is a key indicator for evaluating the dynamic response of vegetation productivity to the spatiotemporal variation in precipitation. It is also an important indicator for reflecting the relationship between the water and carbon cycles in a vegetation ecosystem. This paper uses data from MODIS Net Primary Production (NPP) and China’s spatial interpolation data for precipitation from 2000 to 2015 to calculate the annual value, multiyear mean value, interannual standard deviation, and interannual linear trend of Chinese terrestrial vegetative PUE over the past 16 years. Based on seven major administrative regions, eleven vegetation types, and four climate zones, we analyzed the spatiotemporal variation characteristics of China’s vegetative PUE. The research results are shown as follows: (1) China’s vegetative PUE shows obvious spatial variation characteristics, and it is relatively stable interannually, with an overall slight increasing trend, especially in Northwest and Southwest China. The vegetative PUE is higher, and its stability is declined in Xinjiang, western Gansu, and the southern Tibetan valley. The vegetative PUE is lower, and its stability is increased in northeastern Tibet and southwestern Qinghai. An increasing trend in vegetative PUE is obvious at the edge of the Tarim Basin, in western Gansu, the southern Tibetan valley, and northwestern Yunnan. (2) There is a significant difference in the PUEs among different vegetation types. The average PUE of Broadleaf Forest is the highest, and the average PUE of Alpine Vegetation is the lowest. The stability of the PUE of Mixed Coniferous and Broadleaf Forest is declined, and the stability of the PUE of Alpine Vegetation is increased. The increasing speed of the PUE of Grass-forb Community is the fastest, and the decreasing speed of the PUE of Swamp is the fastest. (3) There is a significant difference in the PUEs among different vegetation types in the same climate zone, the difference in vegetative PUE in arid and semiarid regions is mainly affected by precipitation, and the difference in vegetative PUE in humid and semihumid regions is mainly affected by soil factors. The PUEs of the same vegetation type are significantly different among climate zones. The average PUE of Cultural Vegetation has the largest difference, the stability of the PUE of Steppe has the largest difference, and the increasing speed of the PUE of Swamp has the largest difference.
The Earth’s climate is strongly influenced by the characteristics of atmosphere and ground surface which is known as the ecological environment [
Climate change has obvious impacts on the spatiotemporal distribution of precipitation. Precipitation also greatly influences vegetative activity because fluctuations in interannual precipitation change vegetative biomass [
Vegetative PUE is not only an important variable for linking the carbon and water cycles of vegetative ecosystems but also a way of regulating ecological populations and systems responding to climate change [
The concept of vegetative PUE is proposed based on vegetative water-use efficiency (WUE) [
Spatial processing methods of precipitation data in conventional ground meteorological stations mainly include inverse distance weight (IDW) tension, spline with tension, trend, ordinary kriging, and universal kriging. Many studies have analyzed and discussed the benefits and drawbacks of these methods [
Accuracy comparison of the five interpolation methods.
Interpolation method | Maximum (mm) | Minimum (mm) | Mean (mm) | Standard deviation (mm) |
|
|
---|---|---|---|---|---|---|
IDW | 2686.0908 | 15.8132 | 567.8891 | 474.8156 | 0.2500 | 0.8043 |
Spline | 8519.6455 | −2830.9712 | 560.7704 | 487.1202 | 0.0273 | 0.9784 |
Trend | 4136.6260 | 114.0831 | 577.2636 | 448.7920 | 0.3870 | 0.7014 |
Ordinary kriging | 2613.2412 | −28.6841 | 559.9145 | 476.0661 | 0.3520 | 0.7273 |
Universal kriging | 2201.4834 | −932.9639 | 560.7932 | 472.1631 | 0.2088 | 0.8360 |
China’s climatic regionalization in 1981–2010, as proposed by Zheng et al. [
The maps of (a) arid and humid climate zones and (b) vegetation types in China.
In statistics, the
The
In this section, the general characteristics of vegetative PUE are analyzed; then, there will be contrastive analysis on the difference in the PUEs among different vegetation types by studying Cultural Vegetation, Natural Vegetation, Woody Vegetation, and Herbaceous Vegetation; finally, there will be a comprehensive analysis on the difference in the PUEs among different vegetation types in climate zones and the difference of the same vegetation type’s PUEs among climate zones.
The multiyear mean value of vegetative PUE has obvious spatial variation characteristics in China, as shown in Figure
Temporal variation and spatial distribution of China’s vegetative PUE from 2000 to 2015. (a) Spatial distribution of the multiyear mean value of China’s vegetative PUE. (b) Standard deviation of China’s vegetative PUE. (c) Linear trend of China’s vegetative PUE. (d) Statistical central tendency of the mean value, standard deviation and linear trend of China’s vegetative PUE.
China’s vegetative PUE is stable interannually, but the spatial difference is relatively significant, as shown in Figure
China’s vegetative PUE shows an overall slightly increasing trend, and the rate of increase in the mean values of 2011–2015 is 2.1% compared to those of 2000–2010. From the interannual trend of linear variation, it can be seen that the mean value of the increasing speed of China’s vegetative PUE is 1.1 × 10−3. However, China’s vegetative PUE shows relatively obvious spatial variation characteristics, even decreasing trends in a few regions, accounting for 67.5% of the total land area of the country. The regions with the most obvious decreasing trends are mainly distributed in the western edge of the Tarim Basin, northern Ningxia, eastern Inner Mongolia, southwest of Heilongjiang, northwest of Jilin and other regions in Northwest China, North China, and Northeast China; the regions with the most obvious increasing trends are mainly distributed in the majority of Xinjiang, western Gansu, the southern Tibetan valley, northwest Yunnan and other regions in Northwest China, and Southwest China.
The multiyear mean values of Cultural Vegetative and Natural Vegetative PUEs have obvious spatial variation characteristics, as shown in Figure
Temporal variation and spatial distribution of Cultural Vegetative and Natural Vegetative PUEs from 2000 to 2015. (a) Spatial distribution of the multiyear mean values of Cultural Vegetative and Natural Vegetative PUEs. (b) Standard deviations of Cultural Vegetative and Natural Vegetative PUEs. (c) Linear trends of Cultural Vegetative and Natural Vegetative PUEs.
The PUEs of Cultural Vegetation and Natural Vegetation are relatively stable interannually, but the spatial difference is relatively significant, as shown in Figure
Cultural and Natural Vegetative PUEs show an overall slightly increasing trend, as shown in Figure
Natural Vegetation is divided into two main categories: Woody Vegetation (Coniferous Forest, Mixed Coniferous and Broadleaf Forest, Broadleaf Forest, and Shrub) and Herbaceous Vegetation (Desert, Steppe, Grass-forb Community, Meadow, Swamp, and Alpine Vegetation). The multiyear mean values of Woody Vegetative and Herbaceous Vegetative PUEs have obvious spatial variation characteristics, as shown in Figure
Temporal variation and spatial distribution of different Natural Vegetation types’ PUEs. (a) Spatial distribution of the multiyear mean values of Woody Vegetative and Herbaceous Vegetative PUEs. (b) Standard deviations of Woody Vegetative and Herbaceous Vegetative PUEs. (c) Linear trends of Woody Vegetative and Herbaceous Vegetative PUEs. (d) Statistical central tendency of the mean values, standard deviations, and linear trends of different Natural Vegetation types’ PUEs.
Woody Vegetative and Herbaceous Vegetative PUEs are relatively stable interannually, but the spatial difference is relatively significant, as shown in Figure
Woody Vegetative and Herbaceous Vegetative PUEs show an overall slightly increasing trend, as shown in Figure
The multiyear mean value of vegetative PUE in climate zones has obvious spatial variation characteristics, as shown in Figure
Statistical central tendency of the mean value, standard deviation, and linear trend of vegetative PUE in climate zones from 2000 to 2015: (a) climate zones; (b) humid regions; (c) semihumid regions; (d) arid regions; (e) semiarid regions.
In humid regions, as shown in Figure
In semihumid regions, as shown in Figure
In arid regions, as shown in Figure
In semiarid regions, as shown in Figure
The averages of the multiyear mean values of the same vegetation type’s PUEs among climate zones are significantly different, as shown in Figure
Statistical central tendency of the same vegetation type’s PUEs among climate zones from 2000 to 2015: (a) mean values; (b) standard variations; (c) linear trends.
The interannual PUEs of the same vegetation type among climate zones is stable, but the spatial difference is relatively significant, as shown in Figure
The PUEs of the same vegetation type among climate zones with overall variation trends are significantly different, as shown in Figure
The extreme value regions of vegetative PUE are mainly distributed in arid areas in Northwest China, especially along the edge areas of the Tarim Basin. This is consistent with the findings of Mu et al. [ The Tarim Basin is surrounded by mountains such as the Tianshan, Kunlun, and Altun. Regional runoff is replenished by melting snow and ice from the mountains; thus, the vegetation shows good growth with limited precipitation. Terrain factor may be one of the reasons leading to higher vegetative PUE in the area [ Because the Taklamakan Desert is located in the center of the Tarim Basin, the sand content of the soil surface around the desert is higher; thus, the infiltration rate of precipitation also increases, increasing the vegetative PUE [ In arid areas, the vegetative root system is well developed, with a lower canopy conductance being able to utilize the soil moisture in the lower layer; thus, the production volume of consumed water per unit of vegetation is higher [
The variation in vegetative PUE is closely related to climate zones, soil factors, and vegetation types [ The difference in vegetative PUE may be affected by different vegetation types. The differences in biological community structure, photosynthetic efficiency, and fractional vegetation cover of different vegetation types can result in different PUEs among different vegetation types [ The difference in vegetative PUE may be affected by different climatic zones. For example, a significant spatial difference in the PUEs of the same vegetation type among climatic zones may be due to the distribution of climate conditions, such as heat and moisture, being different in different climatic zones, which can determine vegetative PUE as a zonal distribution [ The difference in vegetative PUE may be affected by precipitation, and water is an important factor that limits vegetative growth in arid areas [ The difference in vegetative PUE may be affected by soil factors in humid areas. Generally, in humid regions, the soil moisture is in a saturation condition with sufficient precipitation, but the biological activity of the soil will be lower. In addition, a superfluous amount of precipitation will result in surface runoff, washing away key nutrient substances that are easily affected by eluviations, such as nitrogen and phosphorus, creating an indirect impact on vegetative growth [
The meteorological stations used in this study are rare in the western China, with a generally uneven distribution that may affect the accuracy of the spatial interpolation of the precipitation data. In the future, the use of remote-sensing data to perform supplemental interpolation of spatial data based on DEM may be preferable to improve the spatial data quality of precipitation. The NPP directly uses the product of MODIS NPP because of the limitation of scale and the lack of measured data verification, which may result in errors when using NPP to calculate PUE. In the future research, we should use this model or improve the relevant model, carry out NPP simulation calculations, and use the measured data for verification, which may improve the accuracy of NPP. In further PUE studies, the NDVI can be used instead of NPP to enable PUE calculations, which may improve PUE quality and provide better data for further research on the spatiotemporal variations of PUE.
Due to the limitations of the research scale and data in this paper, it is impossible to deeply discuss the influencing factors for the evolution of the spatiotemporal patterns of vegetative PUE. As for the relationship between the evolution process of vegetative PUE and climatic change, altitude, biological characters of vegetation, soil, human activities, and so on, we can assess these factors from the following aspects: the driving relationship between changes in vegetative PUE and precipitation and temperature, the difference in vegetative PUE at different altitudes, the correlation between fractional vegetation cover (FVC), leaf area index (LAI) and spatial distribution and interannual fluctuations of vegetative PUE, and the impact of different soil types on spatial differences of vegetative PUE. In future research, other statistical models and analysis methods should be used to further improve the research effectiveness of PUE. In addition, the different influencing factors of vegetative PUE in arid and humid regions also need further in-depth study to gain important findings.
This paper starts from the national scale, using time-series MODIS NPP data and meteorological precipitation data from 2000 to 2015 in China to calculate the Chinese vegetative PUE from 2000 to 2015. Then, it analyzes the changes and spatiotemporal patterns of the Chinese terrestrial vegetative PUE in the most recent 16 years. The main conclusions are as follows: The multiyear mean value of China’s vegetative PUE shows obvious spatial variation characteristics. It is relatively stable interannually, with an overall slightly increasing trend. The regions with extreme values, declined stability, and the most obvious decreasing trends of China’s vegetative PUE all appear along the edge areas of the Tarim Basin. There is a significant difference in the PUEs among different vegetation types. Broadleaf Forest’s PUE is the highest, and Alpine Vegetative PUE is the lowest. The stabilities of Mixed Coniferous and Broadleaf Forest’s and Broadleaf Forest’s PUEs are declined, and the stabilities of Steppe’s and Alpine Vegetative PUEs are increased. The increasing speed of Grass-forb Community’s PUE is the fastest, the decreasing speed of Swamp’s PUE is the fastest, and the increasing speed of Meadow’s PUE tends to be stable. There is a significant difference in the PUEs among different vegetation types in climate zones. No desert exists in humid and semihumid regions. No Mixed Coniferous and Broadleaf Forest or Grass-forb Community exists in arid regions. It includes all vegetation types in semiarid regions. The PUEs of the same vegetation type are significantly different among climate zones. The difference in the average of Cultural Vegetative PUE is the largest, and the difference in the average of Grass-forb Community’s PUE is the least. The difference in the stability of Steppe’s PUE is the largest, and the difference in the stability of Grass-forb Community’s PUE is the least. The difference in the increasing speed of Swamp’s PUE is the largest, and the difference in the increasing speed of Desert’s PUE is the least.
Research on the spatiotemporal patterns of vegetative PUE in China will help us gain an in-depth understanding of the mechanism of vegetation response to global climate change, and we can more clearly recognize the formation process of the productivity of different vegetation types in different climatic zones. The spatiotemporal variation characteristics of vegetative PUE in arid regions, especially the possible influencing factors of vegetative PUE in extreme regions, can provide important references for many researchers of vegetative PUE in arid regions. A relatively comprehensive study of China’s vegetative PUE, based on different vegetation types and different climate zones, can accumulate valuable information for other researchers of China’s vegetative PUE in the future. Vegetative PUE has extensive application prospects in the assessment of vegetation degradation and regional water-carbon cycles. PUE has important practical and theoretical significance for the scientific study of China’s ecological safety constructs, land-vegetation ecosystems, and reaction to global changes.
The vector data of the climate zones and vegetation types in China used to support the findings of this study are included within the article. The MODIS NPP data are downloaded from [NASA,
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was supported by National Key R&D Program of China (no. 2017YFC0506506 and no. 2016YFC0500206) and State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (no. SKLGP2017Z005).