Temporal Variability of Precipitation and Biomass of Alpine Grasslands on the Northern Tibetan Plateau

Abstract

The timing regimes of precipitation can exert profound impacts on grassland ecosystems. However, it is still unclear how the peak aboveground biomass (AGBpeak) of alpine grasslands responds to the temporal variability of growing season precipitation (GSP) on the northern Tibetan Plateau. Here, the temporal variability of precipitation was defined as the number and intensity of precipitation events as well as the time interval between consecutive precipitation events. We conducted annual field measurements of AGBpeak between 2009 and 2016 at four sites that were representative of alpine meadow, meadow-steppe, alpine steppe, and desert-steppe. Thus, an empirical model was established with the time series of the field-measured AGBpeak and the corresponding enhanced vegetation index (EVI) (R2 = 0.78), which was used to estimate grassland AGBpeak at the regional scale. The relative importance of the three indices of the temporal variability of precipitation, events, intensity, and time interval on grassland AGBpeak was quantified by principal component regression and shown in a red–green–blue (RGB) composition map. The standardized importance values were used to calculate the vegetation sensitivity index to the temporal variability of precipitation (VSIP). Our results showed that the standardized VSIP was larger than 60 for only 15% of alpine grassland pixels and that AGBpeak did not change significantly for more than 60% of alpine grassland pixels over the past decades, which was likely due to the nonsignificant changes in the temporal variability of precipitation in most pixels. However, a U-shaped relationship was found between VSIP and GSP across the four representative grassland types, indicating that the sensitivity of grassland AGBpeak to precipitation was dependent on the types of grassland communities. Moreover, we found that the temporal variability of precipitation explained more of the field-measured AGBpeak variance than did the total amount of precipitation alone at the site scale, which implies that the mechanisms underlying how the temporal variability of precipitation controls the AGBpeak of alpine grasslands should be better understood at the local scale. We hypothesize that alpine grassland plants promptly respond to the temporal variability of precipitation to keep community biomass production more stable over time, but this conclusion should be further tested. Finally, we call for a long-term experimental study that includes multiple natural and anthropogenic factors together, such as warming, nitrogen deposition, and grazing and fencing, to better understand the mechanisms of alpine grassland stability on the Tibetan Plateau.

Assessment of the vulnerability of alpine grasslands on the Qinghai-Tibetan Plateau

“…Our result is in line with previous studies showing that grasslands in drier and warmer regions are highly sensitive to precipitation (Li et al, 2019a). Li et al (2019b) also suggested that desert steppes are likely to have high sensitivity to the timing variability of precipitation on the Northern Tibetan Plateau. One potential explanation is that desert steppes are always characterized by poor species and low vegetation productivity due to low precipitation (Wu, Shen & Zhang, 2014), but with high precipitation variation which can result in rapid changes in the key carbon cycle process (Knapp et al, 2002).…”

“…ANUSPLIN is a professional interpolation software in which one or more influence factors can be included as covariates to improve the interpolation accuracy, especially for time series of meteorological data (Hutchinson, 2004). Our previous studies have shown the accuracy of interpolation data (Chen et al, 2014;Li et al, 2019b).…”

Impacts of Climate Change on Lake Fluctuations in the Hindu Kush-Himalaya-Tibetan Plateau

“…Previous studies [6,51,60,61] have reported the rising river flows due to glacier retreat in the HKHT regions. However, our results showed that the river flows and GMC fluctuations have no consistent trends but rather they are mainly controlled by the changing climate, especially the precipitation (despite the significant differences in precipitation patterns over basins and catchments) [44]. In the rainfall-runoff-dominated HKHT Interior and Yellow River basin, the insufficient precipitation has derived two contradictory trends: Increased GMC but decreased flows and shrinking lakes in the same period.…”

“…The causes of lake fluctuation remain tangled due to elusive precipitation trends in the high-altitude regions [43,44]. The data indicates that temperature increases (0.1~0.6 per decade) in the HKHT regions are more pronounced compared to the lower elevation regions [1,45], thus leading to a rapid retreat of glaciers in recent decades [3,46].…”

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

“…Based on the NDVI data from 1982 to 2014, an advancing start of growing season, delayed end of season and increasing length of growing season were identified for meadow areas in the eastern TP; while the opposite changes were found for the steppe and sparse herbaceous or sparse shrub areas in the northwest and western edges of the TP. The satellite-observed phenology changes were driven by a number of environmental factors including temperature [281][282][283], precipitation [284], sunshine duration [285], and snow cover [286]; and may be partially attributed to aerosol contamination in the satellite observations [287].…”