Appropriated protection time and region for Qinghai–Tibet Plateau grassland

Shuan Qian; Feifei Pan; Menxin Wu; Yinglong Sun

doi:10.1515/geo-2022-0383

Article Open Access

Appropriated protection time and region for Qinghai–Tibet Plateau grassland

Shuan Qian , Feifei Pan , Menxin Wu and Yinglong Sun

Published/Copyright: July 25, 2022

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal Open Geosciences Volume 14 Issue 1

Abstract

Grassland accounts for 67% of the land area in Qinghai–Tibet Plateau (QTP) to sensitive to climate. This article carried out extensive temporal–spatial variations of grassland water deficit/surplus (GWDS) and net primary productivity (NPP) of QTP grassland using meteorological data of 1981–2018. The results indicate that precipitation’s temporal variations are not related to potential evapotranspiration (PET). The maximum monthly precipitation occurs in July, while the maximum monthly PET appears in June. Precipitation is the second highest in spring when PET is the highest. The water deficit (PET-precipitation) from March to June takes up 51.4% of the total year. Therefore, droughts are more likely to happen in spring and June. This is the most important period for grassland protection. Water deficit is higher in the central and west and the highest in the northwest of QTP grasslands. This indicates the place where the droughts are more likely to occur. The grassland’s annual NPP is high in the southeastern part of QTP and low in the west. This shows the central and western parts of QTP grassland should receive more attention. The specific time and region obtained in this study are important for environmental protection policy-making and reducing grassland degradation in QTP.

Keywords: precipitation; potential evapotranspiration; water deficit/surplus; specific protective time and region; Qinghai–Tibetan Plateau grassland

1 Introduction

With an area of about 2572.4 × 10³ km², the Qinghai–Tibetan Plateau (QTP) is the largest and highest plateau in the world [1,2]. In this region, the grassland covers about 67% of the land. The distribution of grassland types and productivity are affected by local climate [3]. Understanding the climate and its impact on grassland productivity is critical for environmental protection policy-making and grassland restoration.

To better understand QTP’s effect on weather and climate in China, atmospheric science experiments in this region have been conducted [4]. The impacts of climate change on vegetation productivity have been studied extensively in recent years [5,6,7,8,9,10,11,12,13,14]. However, most previous studies only focused on weather and climate forecasts and the change in annual temperature, precipitation, and vegetation production. Grassland vegetation production was estimated with remote sensing techniques and meteorological data [15,16,17,18,19,20]. Grassland net primary productivity (NPP), an important indicator to monitor vegetation health, was estimated with monthly meteorological data and normalized difference vegetation index (NDVI) of satellite data based on the models of solar energy efficiency [20,21]. Daily meteorological data were also adopted to estimate grassland NPP in grassland ecological models [22,23]. Moreover, grassland productivity responses to climate change were studied in some regions and the entire QTP [23,24].

Previous studies are significant, but they have not covered the temporal and spatial variations of precipitation, the potential evapotranspiration (PET), the difference between PET and precipitation (i.e., water deficit/surplus), and their impacts on grassland productivity of 1981–2018 in the QTP. Especially, the farmland water deficit in China was studied [25], but the grassland water deficit (GWD) was not known. Drought evolution characteristics of the QTP over the last 100 years based on Standardized Precipitation Evapotranspiration Index (SPEI) were calculated using the CRU4.03 grid data set of 1901–2018. Then, according to the SPEI data, a drought of the spatial and temporal distribution of the QTP and the changing characteristics of nearly 100 years were studied [26]. But this study was for seasonal and annual, and used data in GRID data, is not based on actual observational data and can be difficultlly used to guide grassland ecological protection. Attribution of the Tibetan Plateau to northern drought was studied [27], but the drought of the QTP was not studied. Drought characteristics and its elevation dependence in the QTP during the last half-century were studied [28], but which discussed the drought change trend over the past many years. As the specific time and region for frequent droughts in the month and season of a year for grassland are not known, it is difficult to protect QTP grassland appropriately.

Degradation of grasslands will significantly damage the ecosystem [29,30], affecting grazing intensity, soil seed bank, and above-ground vegetation [31]. To combat degradation and protect the grasslands, it is urgent to find the right protective time and place for the grasslands of the Tibetan Plateau. This study has systematically measured the temporal–spatial distributions and variations of precipitation, PET, and water deficit (surplus status) and evaluated their impacts on grassland productivity. Using daily meteorological data of 1981–2018 in the QTP, the time and region of frequent droughts has been determined. This study aims to help environmental protection policy-making and the reduction of grassland degradation in QTP [32,33].

2 Methods

2.1 Study area

Grassland ecosystem is a major type of the QTP terrestrial ecosystem. Its condition will influence the ecological environment and animal husbandry. Due to different climates in the QTP, the spatial distribution of grasslands shows zonal patterns from the southeast to the northwest [3]. Alpine meadow is the most representative vegetation of the QTP, counting for 41.2% of the grassland’s area. Alpine steppe is the second largest ecosystem found in the QTP, where it makes up 32.4% of the total. The study area is the grassland ecosystem in the QTP.

2.2 Data and materials

Daily meteorological data of 1981–2018 are collected at 90 meteorological stations across the QTP. The data include daily maximum, minimum, and mean air temperatures, precipitation, relative humidity, total cloud cover, wind speed, sunlight hours, and vapor pressure. Based on the daily data, monthly, seasonal, and annual meteorological values are calculated (Spring: March–May; Summer: June–August; Autumn: September–November; Winter: December–February; Grass-growing season: April–September).

Soil data are obtained from the Nanjing Institute of Soil Science, Chinese Academy of Sciences (NISSCAS). Based on the 0.1° × 0.1° soil map and each meteorological station’s latitude and longitude, the soil texture (i.e., sand, clay, and silt percentages) at each station is determined and entered into the AVIM-GRASS model [23].

A grassland digital map of 1:4 million scales is used to determine grassland type at each metrological station. The data are obtained from the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) of the Chinese Academy of Sciences (CAS), serving as another input variable in the AVIM-GRASS model.

2.3 Estimation of daily PET of grassland

Evapotranspiration influences many ecological processes of the grassland. The FAO Penman–Monteith method is recommended as the sole method for determining grassland PET [25,34,35]. Therefore, formula (1) is used to calculate QTP daily PET (mm day⁻¹) of 1981–2018.

(1) PET = 0.408 Δ ( R n − G n ) + γ 900 T + 273 U 2 ( e s − e a ) Δ + γ ( 1 + 0.34 U 2 ) ,

where R _n denotes the net radiation reaching the earth’s surface (MJ m⁻² day⁻¹), which was calculated as formula (2); G _n represents the soil heat flux (MJ m⁻² day⁻¹), in the estimation of daily evapotranspiration, and G_n = 0; T is the air temperature (°C) at 2 m above ground level; U ₂ shows the wind speed at 2 m high (m s⁻¹); e _s is the saturation vapor pressure (kPa); e _a is the actual water vapor pressure (kPa); e _s − e _a signifies the saturated vapor pressure difference (kPa); ∆ indicates the slope of saturation vapor pressure at temperature T (kPa °C⁻¹); γ represents the humidity constant (kPa °C⁻¹).

(2) R n = ( 1 − a ) R s − S σ ( 273 + T ) 4 ( 0.39 − 0.058 e a ) ( 0.10 + 0.90 n / N ) ,

where R _n denotes the net radiation reaching the earth’s surface (MJ m⁻² day⁻¹); a is the grassland reflectivity; R _s is the global solar radiation (MJ m⁻² day⁻¹); S is the number of graybody emission, taking 0.9: σ is for the Stephen–Boltzmann constant, taking 2.01 × 10⁻⁹; T is for the air temperature (°C) at 2 m above ground level; e _a is for the actual water vapor pressure (kPa); n/N is for the percentage of sunshine.

2.4 Estimation of grassland water deficit/surplus (GWDS)

Water balance (Inflow = Outflow ± Changes in Storage) reflects water deficit/surplus in the grassland. The major input of water in the QTP grassland derives from precipitation, glacial melt and snowmelt, streams and rivers, and groundwater; however, the major output of water is due to evapotranspiration and outflow of runoffs. Compared with precipitation and evapotranspiration, the factors of glacial melt, snowmelt, and runoffs contribute far less [36,37] to the QTP water balance, so they are not considered in this study. Therefore, the water balance is mainly controlled by precipitation and evapotranspiration [38].

According to the reference, multi-year average difference between precipitation and PET can reflect water deficit/surplus [25,39,40,41,42,43]. Therefore, the difference between precipitation and PET is used to express the GWDS. GWDS during the stage j in year i is calculated as follows:

(3) GWDS i , j = P i , j − PET i , j , if P i,j ≥ PET i , j GWDS = GWS if P i , j < PET i , j GWDS = GWD,

where P _i,j and PET_i,j are precipitation and evapotranspiration during stage j of year i, respectively. If P _i,j ≥ PET_i,j, GWDS_i,j indicates the grassland water surplus (GWS). If it is zero, GWDS_i,j means that precipitation basically meets evapotranspiration demand. If P _i,j < PET_i,j, GWDS_i,j denotes the grassland water deficit (GWD).

2.5 Estimation of daily NPP of grassland

Daily NPP of the grassland vegetation is estimated by the AVIM-GRASS model [22,23], through the simulation of climate impacts on the grassland vegetation. Driven by daily meteorological data, the model includes land surface processes and plant physiological processes. The simulated NPP is then calibrated against the measurements of the herbage mass [23]. The daily NPP (gC m⁻²) is calculated as follows:

(4) NPP j = GPP j − ∑ RM j − ∑ RG j ,

(5) GPP j = 1 − e K ⋅ LAI K A ,

where NPP_j is the NPP (gC m⁻²) on day j; GPP_j represents gross photosynthesis (gC m⁻²) on day j; ∑ RM j and ∑ RG j signify the amount of maintenance respiration and growth respiration on day j (gC m⁻²) all of the grass organs, respectively; K indicates the canopy light extinction coefficient of grassland vegetation; LAI refers to the grassland leaf area index; and A is leaf photosynthesis rate (gC m⁻²).

3 Results

3.1 Temporal and spatial variation of precipitation

Figure 1 shows the mean monthly and quarterly precipitation of the QTP grassland. The monthly precipitation begins to increase in January to over 10 mm in March and peaks in July. It starts to fall in August and quickly drops until November. The precipitation of April–September accounts for 89.4% of the annual precipitation. Among four seasons, precipitation is the most abundant in summer, accounting for 61.2% of the annual precipitation, followed by autumn and spring. The precipitation mainly occurs in the grass-growing season, especially in summer. This is consistent with the growth pattern of the QTP grassland vegetation.

Figure 1

Variation of mean monthly (a and c), seasonal precipitation (b and d), and their proportions of mean annual precipitation for QTP grassland.

The spatial distribution of precipitation in the grass-growing season gradually decreases from the southeast to the northwest in the QTP. Precipitation is around 400–700 mm in eastern Tibet and southeastern Qinghai, but less than 100 mm in the western part of the QTP (Figure 2).

Figure 2

Spatial distribution of precipitation during the grass-growing season for the QTP grassland.

3.2 Temporal and spatial variation of PET

Monthly PET has a single peak and is above 30 mm in January–December (Figure 3). As the temperature starts rising in February, the grassland PET increases accordingly, reaching the highest value in June. PET begins to decline in July and reaches the lowest value in December. Among the four seasons, grassland PET is the highest in summer and the second highest in spring. Winter witnesses the lowest PET. Moreover, PET of the grass-growing season accounts for 68.1% of the annual value [44].

Figure 3

Variation of mean monthly (a and c), seasonal PET (b and d) and their proportions of mean annual PET for QTP grassland.

During the grass-growing season, PET in the QTP follows a noticeable spatial pattern: it gradually increases from the southeast to the northwest. PET in southeastern Qinghai and eastern Tibet is around 650–800 mm. In comparison, PET in northwestern Qinghai and mid-western Tibet is around 900–1,100 mm (Figure 4). It can be seen that the spatial distribution and pattern of the evapotranspiration volume are opposite to those of precipitation (Figures 2 and 4).

Figure 4

Spatial distribution of PET during the grass-growing season for the QTP grassland.

3.3 Temporal and spatial variation of the GWD/GWS status

The water supply for grassland vegetation mainly comes from precipitation, while the main water expenditure is evapotranspiration. The difference between these two variables reflects the GWDS. Figure 5 shows monthly and seasonally mean GWDS from 1981–2018. The monthly GWD is above 30 mm from January to December. Therefore, the QTP grassland faces a water deficit throughout the year. GWD starts to go up gradually from January to May. It reaches the highest value of 91 mm in May and slightly down to 80 mm in June. GWD is the most severe from March to June (spring to early summer). However, GWD decreases rapidly after May and reaches below 40 mm from July to September. The lowest value happens in September. Although GWD rebounds to 55 mm in October, the monthly value is still lower than values from March to June. GWD stays at a low level during November and December. Therefore, the period from March to June, when the GWD is relatively high, is the time with more droughts in the QTP grassland.

Figure 5

Variation of mean monthly (a and c), seasonal GWDS (b and d) and their proportions of mean annual GWDS for the QTP grassland.

In the QTP grassland, precipitation is less than the corresponding PET during four seasons (Figure 5). GWD ranks from the highest to the lowest during spring (up to 244 mm), summer (145 mm), autumn (130 mm), and winter (124 mm). GWD accounts for 64.6% of the annual water deficit from January to June, and 51.4% from March to June. GWD is higher in the first half of the year, especially from March to June (spring to early summer), during which half of the annual water deficit is registered. Thus, severe and frequent droughts often happen during this period. The most vigorous growth of plants is just from spring to early summer when the grassland needs more water than during other periods. However, drought easily causes greater effects on grassland vegetation growth.

As for the spatial distribution, the GWD occurs in most of the QTP grassland during the grass-growing season from April to September (Figure 6). The GWD increased from the southeast to the northwest in the QTP, and it was below 400 mm in the southeast Qinghai and east Tibet. The GWD was higher in the northwestern Qinghai and west Tibet, which was 600–1,000 mm, the most severe area.

Figure 6

Spatial distribution of GWDS during the grass-growing season for the QTP grassland.

3.4 Temporal and spatial variation of NPP

The monthly and seasonal mean NPPs of 1981–2018 are shown in Figure 7. The temporal change of monthly NPP was in accordance with monthly precipitation. NPP increases from January, jumps from April to July, peaks in July, begins to fall in August, and plummets until November. This indicates that precipitation is the primary driver of the grassland NPP, especially during the major grass-growing season. When the cumulative rainfall reaches 363 mm, the cumulative NPP hits 241.4 gC m⁻². Among four seasons, grassland NPP is the highest in summer, up to 143.8 gC m⁻². Seasons with the second largest, the moderate, and the least grassland NPP, are spring, autumn, and winter, respectively.

Figure 7

Variation of mean monthly (a and c), seasonal NPP (b and d) and their proportions of mean annual NPP for QTP grassland.

The spatial distribution of annual NPP is related to precipitation and GWDS (Figure 8). NPP slowly declines from the southeast to the northwest of QTP and is opposite to spatial patterns of PET and GWD. The mean annual NPP is between 300 and 600 gC m⁻² in parts of eastern and southern Qinghai and most of eastern Tibet. It even reaches 600–900 gC m⁻² in some areas. In comparison, the mean annual NPP is below 200 gC m⁻² in the northwestern Qinghai and western Tibet.

Figure 8

Spatial distribution of annual NPP for the QTP grassland.

4 Discussion

Meteorological station data are used to analyze the temporal and spatial variations of GWDS and grassland vegetation productivity in the QTP. The results show the detailed temporal and spatial difference of precipitation, PET, GWDS and their influences on the grassland productivity. Compared with previous studies, this study provides more quantitative values about the time and region of GWD in the month and season, it has found places and time with more frequent droughts. The AVIM-GRASS model driven by daily meteorological data gave the climate impacts on the grassland vegetation NPP during 12 months and 4 seasons, especially GWD and the drought which is infrequent and important. It is helpful for the protection and management of QTP regional ecology, and the methods it has adopted are also suitable to study water deficit change trends and impacts on grassland productivity in the future. The article can guide measure-making in grazing, fallow, and fencing for a reasonable grazing system with moderate grazing intensity. Overall, it contributes to protect the ecosystem of the QTP grassland.

5 Conclusion

This study reveals the temporal–spatial distributions of precipitation, PET, the GWDS, and the grassland productivity in the QTP grassland. It has thoroughly studied the difference between precipitation and PET, the time of more water deficit and droughts, and grassland NPP of this region so that we can determine the appropriate time and place to protect the QTP grassland. This is very important for making ecological protection policy and protective measures to restore the degraded grassland ecosystems in the QTP.

5.1 The appropriate time for the QTP grassland protection

From January to December, precipitations in the QTP grassland are less than PET. It means that the grassland water is in deficit throughout the year and that may cause droughts at any month. Meanwhile, the temporal variations of monthly and seasonal precipitation do not correspond well to those of PET, and the peak times of precipitation and PET do not coincide. This indicates that the water supply does not always meet the demand. This is another cause of droughts. According to our study, the GWD is higher in March–June and highest in May. Therefore, the grassland drought happens more easily in spring and June. As the grassland starts to regreen in spring and grows vigorously from June, much water is needed during this period. Therefore, the period of March–June needs much attention to targeted measures to protect the QTP grassland, such as closure against grazing.

5.2 The main protecting region for the QTP grassland

There are noticeable differences in the spatial distributions of precipitation, PET, the GWDS, and the grassland NPP. GWDS of the grass-growing season is a deficit for most of QTP (Figure 6). The northwest of the QTP grassland has the highest GWD and the lowest annual NPP. Therefore, this place should receive more protection.

Funding information: This article were supported by the Development of Meteorological Satellite Remote Sensing Technology and Platform for Global Monitoring, Assessments and Applications of National Key Research and Development Program of China (2018YFC1506500), Demonstration Project of the Meteorological Monitoring and Evaluation Capacity of the China Meteorological Administration to Guarantee Ecological Civilization (ZQH2017263), Ecological meteorological monitoring and evaluation innovation project of National Meteorological Center (NMC225020400214), University of North Texas Research Initiative Grant (RIG).
Author contributions: This manuscript is contributed by Shuan Qian, Feifei Pan, Menxin Wu and Yinglong Sun; analysed and revised by Shuan Qian, Feifei Pan; and data processing by Menxin Wu and Yinglong Sun.
Conflict of interest: The authors state no conflict of interest.

References

[1] Zhang YL, Li BY, Zheng D. A discussion on the boundary and area of the Tibetan Plateau in China. Geographical Res. 2002;1:1–8.Search in Google Scholar

[2] Zhuang Q, He J, Lu Y, Ji L, Xiao J, Luo T. Carbon dynamics of terrestrial ecosystems on the Tibetan Plateau during the 20th century: an analysis with a process-based biogeochemical model. Glob Ecol Biogeograp. 2010;19:649–62.10.1111/j.1466-8238.2010.00559.xSearch in Google Scholar

[3] Animal Husbandry and Veterinary Medicine Division (AHVMD). Ministry of Agriculture of the People’s Republic of China. China Grassland Resources. Chinese Science and Technology Press; 1996.Search in Google Scholar

[4] Zhao P, Li Y, Guo X, Xu X, Liu Y, Tang S, et al. The Tibetan Plateau surface–atmospheric coupling system and its weather and climate effects: The third Tibetan Plateau atmospheric science experiments. J Meteorolgical Res. 2019;33(3):375–99.10.1007/s13351-019-8602-3Search in Google Scholar

[5] Fang JY, Yoda K. Climate and vegetation in China III water balance and distribution of vegetation. Ecol Res. 1990;1:9–23.10.1007/BF02348460Search in Google Scholar

[6] Melillo JM, McGuire AD, Kicklighter DW. Global climate change and terrestrial net primary production. Nature. 1993;363:234–40.10.4324/9781315793245-24Search in Google Scholar

[7] Nemani RR, Keeling CD, Hashimoto H. Climate-driven increases in global terrestrial netprimary production from 1982 to 1999. Science. 2003;300:1560–3.10.1126/science.1082750Search in Google Scholar PubMed

[8] Buono G, Oesterheld M, Nakamatsu V, Paruelo JM. Spatial and temporal variation of primary production of Patagonian wet meadows. J Arid Environ. 2010;74:1257–61.10.1016/j.jaridenv.2010.05.026Search in Google Scholar

[9] Thomey ML, Collins SL, Vargas R, Johnson JE, Brown RF, Natvig DO, et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan desert grassland. Glob Change Biol. 2011;17:1505–15.10.1111/j.1365-2486.2010.02363.xSearch in Google Scholar

[10] Hsu JS, Powell J, Adler PB. Sensitivity of mean annual primary production to precipitation. Glob Change Biol. 2012;18:2246–55.10.1111/j.1365-2486.2012.02687.xSearch in Google Scholar

[11] Parton W, Morgan J, Smith D, Del Grosso S, Prihodko L, LeCain D, et al. Impact of precipitation dynamics on net ecosystem productivity. Glob Change Biol. 2012;18:915–27.10.1111/j.1365-2486.2011.02611.xSearch in Google Scholar

[12] Eisfelder C, Klein I, Niklaus M, Kuenzer C. Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. J Arid Environ. 2014;103:17–30.10.1016/j.jaridenv.2013.12.005Search in Google Scholar

[13] Mao DH, Wang ZM, Li L, Ma W. Spatiotemporal dynamics of grassland aboveground net primary productivity and its association with climatic pattern and changes in Northern China. Ecol Indic. 2014;41:40–8.10.1016/j.ecolind.2014.01.020Search in Google Scholar

[14] Liang W, Yang Y, Fan D, Guan H, Zhang T, Long D, et al. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric For Meteorol. 2015;204:22–36.10.1016/j.agrformet.2015.01.015Search in Google Scholar

[15] Piao SL, Fang JY, He JS, Xiao Y. Spatial distribution of grassland biomass in China. Acta Phytoecolog Sin. 2004;4:491–8.10.17521/cjpe.2004.0067Search in Google Scholar

[16] Qian S, Mao LX, Hou YY, Wu MX, Wang LY. Technology and application of ecological and meteorological synthesis for monitoring and predicting in northern grasslands in China. Meteorol Monthly. 2008;11:62–8.Search in Google Scholar

[17] Qian S. Grass yield meteorological prediction method for main natural grasslands in China. Chinese J Ecol. 2009;28(6):1201–5.Search in Google Scholar

[18] Yan LD, Guo JP, Qi DL, Li L, Su SL. Meteorological drought in Qinghai Province: impact on pasture output and application in pasture yield estimation. J Glaciol Geocryol. 2016;38(6):1732–8.Search in Google Scholar

[19] Wang S, Guo B, Zhang HH, Wang MT. Estimation Model for the Natural Herbage Yield in Zoige Wetland:Based on Meteorological Suitability Index. Chin Agric Sci Bull. 2017;33(25):93–8.Search in Google Scholar

[20] Gao QZ, Wang YF, Li YE, Lin ED, Yang K, Jiang CWZ, et al. Grassland net primary production and its spatiotemporal distribution in northern Tibet: a study with CASA model. Chinese. J Appl Ecol. 2018;11:2526–32.Search in Google Scholar

[21] Hou YY, Mao LX, Li CS, Qian S. Spatiotemporal variation pattern of vegetation’s net primary productivity in China. Chin J Ecol. 2008;9:1455–60.Search in Google Scholar

[22] Ji JJ. A climate vegetation interaction model: simulating physical and biological processes at the surface. J Biogeograp. 1995;22:445–51.10.2307/2845941Search in Google Scholar

[23] Qian S, Wang LY, Gong XF. Climate change and its effects on grassland productivity and carrying capacity of livestock in the main grasslands of China. Rangel J. 2012;4:341–7.10.1071/RJ11095Search in Google Scholar

[24] Li WH, Zhao XQ, Zhang XZ, Shi PL, Wang XD, Zhao L. Change mechanism in main ecosystems and its effect of carbon source/sink function on the Qinghai-Tibetan Plateau. Chin J Nat. 2013;3:172–8.Search in Google Scholar

[25] Hou GL, Du MY, Liu YF, Chen SB, Ni JH. Division of water deficit in China. Agric Meteorol. 1988;9:157–64.Search in Google Scholar

[26] Wang S, Liu F, Zhou Q, Chen Q, Niu B, Xia X. Drought evolution characteristics of the Qinghai-Tibet Plateau over the last 100 years based on SPEI. Nat Hazards Earth Syst Sci Discuss; 202110.5194/nhess-2021-73Search in Google Scholar

[27] Liu Y, Li Y, Huang J, Zhu Q, Wang S. Attribution of the Tibetan Plateau to northern drought. Natl Sci Rev. 2019;7:489–92.10.1093/nsr/nwz191Search in Google Scholar PubMed PubMed Central

[28] Feng W, Lu H, Yao T, Yu Q. Drought characteristics and its elevation dependence in the Qinghai-Tibet plateau during the last half-century. Sci Rep. 2020;10:14323.10.1038/s41598-020-71295-1Search in Google Scholar PubMed PubMed Central

[29] Xu Y, Dong S, Shen H, Xiao J, Li S, Gao X, et al. Degradation significantly decreased the ecosystem multifunctionality of three alpine grasslands: evidences from a large-scale survey on the Qinghai-Tibetan Plateau. J Mt Sci. 2021;18:357–66.10.1007/s11629-020-6472-xSearch in Google Scholar

[30] Geng M, Ma K, Sun Y, Wo X, Wang K. Changes of land use/cover and landscape in Zhalong wetland as “red-crowned cranes country”, Heilongjiang province, China. Glob NEST J. 2020;22(4):477–83.Search in Google Scholar

[31] Chu H, Zhang C, Dong Q, Shang Z, Degen AA, Yang X, et al. The effect of grazing intensity and season on the soil seed bank and its relation with above-ground vegetation on the alpine steppe. Agric Ecosyst Environ. 2019;285:106622.10.1016/j.agee.2019.106622Search in Google Scholar

[32] Chen X, Quan Q, Zhang K, Wei J. Spatiotemporal characteristics and attribution of dry/wet conditions in the Weihe River Basin within a typical monsoon transition zone of East Asia over the recent 547 years. Environ Model Softw Environ Data N. 2021;143:105116.10.1016/j.envsoft.2021.105116Search in Google Scholar

[33] Quan Q, Liang W, Yan D, Lei J. Influences of joint action of natural and social factors on atmospheric process of hydrological cycle in Inner Mongolia, China. Urban Clim. 2022;41:101043.10.1016/j.uclim.2021.101043Search in Google Scholar

[34] Allen RG, Pereira LS, Raes D, Smith M. Crop Evapotranspiration: Guidelines for computing crop water requirements. FAO Irrig Drain Pap. 1998;29–78.Search in Google Scholar

[35] Li CM, Gao SH. Study on the moisture budget of the grasslands in the semiarid regions in north China. Arid Zone Res. 2004;4:338–42.Search in Google Scholar

[36] Guo R, Wang XK, Ouyang ZY, Li YN. Spatial and temporal relationships between precipitation and ANPP of four types of grasslands in northern China. J Environ Sci. 2006;5:1024–30.10.1016/S1001-0742(06)60033-8Search in Google Scholar

[37] Wang GX, Liu GS, Li CJ. Effects of changes in alpine grassland vegetation cover on hillslope hydrological processes in a permafrost watershed. J Hydrol. 2012;444–445:22–33.10.1016/j.jhydrol.2012.03.033Search in Google Scholar

[38] Liu Y, Zhang K, Li Z, Liu Z, Wang J, Huang P. A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J Hydrol (Amst). 2020;590:125440.10.1016/j.jhydrol.2020.125440Search in Google Scholar

[39] Shi JG, Yan CR, He WQ, Liu KL. Study on spatial and temporal variation of water surplus and deficiency in Yellow River Basin. J Nat Resour. 2008;1:113–9.Search in Google Scholar

[40] Zeng LH, Song KS, Zhang B. Spatial and temporal structure of water deficit over northeast China during the period of 1951-2008. J Nat Resour. 2011;5:858–70.Search in Google Scholar

[41] Zhao X, Xia H, Pan L, et al. Drought monitoring over Yellow River basin from 2003–2019 using reconstructed MODIS land surface temperature in google earth engine. Remote Sens (Basel, Switzerland). 2021:13(18);3748.10.3390/rs13183748Search in Google Scholar

[42] Yao XJ, Zhang X, Sun MP, Li J, Wang WD. Temporal-spatial characteristics of water deficit in Northwest China from 1960 to 2010. Geograph Res. 2013;4:607–16.Search in Google Scholar

[43] Mao F, Tang SH, Sun H, Zhang JH. A study of dynamic change of dry and wet climate regions in the Tibetan Plateau over the last 46 years. Chin J Atmos Sci. 2008;32:499–507.Search in Google Scholar

[44] Li J, Charles LS, Yang Z, Du G, Fu S. Differential mechanisms drive species loss under artificial shade and fertilization in the alpine meadow of the Tibetan Plateau. Front Plant Sci. 2022;13:832473.10.3389/fpls.2022.832473Search in Google Scholar PubMed PubMed Central

Received: 2022-02-10

Revised: 2022-03-22

Accepted: 2022-05-20

Published Online: 2022-07-25

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0383

Keywords for this article

precipitation; potential evapotranspiration; water deficit/surplus; specific protective time and region; Qinghai–Tibetan Plateau grassland

Creative Commons

BY 4.0