2015
DOI: 10.3390/rs71215847
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Climatic Controls on Spring Onset of the Tibetan Plateau Grasslands from 1982 to 2008

Abstract: Understanding environmental controls on vegetation spring onset (SO) in the Tibetan Plateau (TP) is crucial to diagnosing regional ecosystem responses to climate change. We investigated environmental controls on the SO of the TP grasslands using satellite vegetation index (VI) from the 3rd Global Inventory Modeling and Mapping Studies (GIMMS3g) product, with in situ air temperature (T a ) and precipitation (Prcp) measurement records from 1982 to 2008. The SO was determined using a dynamic threshold method base… Show more

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Cited by 33 publications
(17 citation statements)
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“…However, the considerable difference in the timing of earliest greening in 2013 and 2014 (beginning of May) versus 2015 (beginning of June), in combination with the clear decline in temperature sensitivity in 2015, indicated that day length was not the main driver of the SOS in these subarctic grasslands. Precipitation can play a role in SOS of subarctic and alpine grasslands, although its effect is not consistent, varying between nonexistent (Piao et al., ), positive (Fu, Piao, et al., ), negative (Chen et al., ; Sha, Zhong, Bai, Tan, & Li, ), and dependent on the specific situation (Shen, Piao, Cong, Zhang, & Janssens, ; Shen, Tang, Chen, Zhu, & Zheng, ; Zhang, Yi, Kimball, Kim, & Song, ). In this study, no major variation in soil water status occurred along the temperature gradients (Sigurdsson et al., ), especially in early spring, so it is unlikely that precipitation was an important determinant of the decelerating advance of SOS.…”
Section: Discussionmentioning
confidence: 99%
“…However, the considerable difference in the timing of earliest greening in 2013 and 2014 (beginning of May) versus 2015 (beginning of June), in combination with the clear decline in temperature sensitivity in 2015, indicated that day length was not the main driver of the SOS in these subarctic grasslands. Precipitation can play a role in SOS of subarctic and alpine grasslands, although its effect is not consistent, varying between nonexistent (Piao et al., ), positive (Fu, Piao, et al., ), negative (Chen et al., ; Sha, Zhong, Bai, Tan, & Li, ), and dependent on the specific situation (Shen, Piao, Cong, Zhang, & Janssens, ; Shen, Tang, Chen, Zhu, & Zheng, ; Zhang, Yi, Kimball, Kim, & Song, ). In this study, no major variation in soil water status occurred along the temperature gradients (Sigurdsson et al., ), especially in early spring, so it is unlikely that precipitation was an important determinant of the decelerating advance of SOS.…”
Section: Discussionmentioning
confidence: 99%
“…The delayed trend may be the product of increasingly severe aridity, unmet chilling or photoperiod requirements for plants [57], and a cooling trend in spring air temperatures [23]. Some studies have reported that SOS has continuously occurred earlier in the Tibetan Plateau [58]; the discrepancies among results may be the product of differences in vegetation index responses to vegetation growth between sensors [59] and/or measurements for estimating phenology. Furthermore, SOS trends in the Tibetan Plateau may vary with longitude [16] and elevation [23] due to variation in vegetation types and climate conditions; thus, the considerable spatial heterogeneity of the Tibetan Plateau may offset the positive and negative change trends observed among pixels [13], and the trend may not be significant or even distinguishable over the entire study area.…”
Section: Temporal Changes In Phenology At the Regional Scalementioning
confidence: 99%
“…The GIMMS3g NDVI record has an 8-km spatial resolution and a 15-day temporal interval, and was assembled from different NOAA Advanced Very High Resolution Radiometer (AVHRR) records accounting for various deleterious effects including calibration loss, orbital drift and volcanic eruptions [37]. The dynamic threshold method, which defined the threshold of vegetation growing-season onset and offset as 25% of the seasonal NDVI amplitude based on the multi-year average [2,38], was used to derive growing season onset, offset and duration [2,14,38]. Prior to the phenology analysis, the NDVI time series were filtered using the double logistic method from the Timesat Tool to remove outliers and fill data gaps [38].…”
Section: Remote Sensing Measurementsmentioning
confidence: 99%