No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau

Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth’s “third pole,” is a unique region for studying the long-term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low-level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l’Observation de la Terre Vegetation (SPOT-VEG) NDVI data set (1999–2013), and the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green-up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground-based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.

[1]  Xiaoqiu Chen,et al.  Temperature and snowfall trigger alpine vegetation green‐up on the world's roof , 2015, Global change biology.

[2]  Miaogen Shen,et al.  Spring phenology was not consistently related to winter warming on the Tibetan Plateau , 2011, Proceedings of the National Academy of Sciences.

[3]  Liyun Dai,et al.  Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China , 2015, Remote. Sens..

[4]  Song Gu,et al.  Stability of alpine meadow ecosystem on the Qinghai-Tibetan Plateau , 2006 .

[5]  R. Fensholt,et al.  Evaluating temporal consistency of long-term global NDVI datasets for trend analysis , 2015 .

[6]  Yanhong Tang,et al.  Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau , 2011 .

[7]  Jinwei Dong,et al.  Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 , 2013, Proceedings of the National Academy of Sciences.

[8]  Damon S. Hartley,et al.  Effects of seasonal snow on the growing season of temperate vegetation in China , 2013, Global change biology.

[9]  X. Zhao,et al.  Field evidence for earlier leaf-out dates in alpine grassland on the eastern Tibetan Plateau from 1990 to 2006 , 2014, Biology Letters.

[10]  Eike Luedeling,et al.  Winter and spring warming result in delayed spring phenology on the Tibetan Plateau , 2010, Proceedings of the National Academy of Sciences.

[11]  P. Ciais,et al.  Using satellite data to improve the leaf phenology of a global terrestrial biosphere model , 2015 .

[12]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[13]  Philippe Ciais,et al.  Declining global warming effects on the phenology of spring leaf unfolding , 2015, Nature.

[14]  Liangyun Liu,et al.  Effects of elevation on spring phenological sensitivity to temperature in Tibetan Plateau grasslands , 2014 .

[15]  W. Debruyn,et al.  15 years of processing and dissemination of SPOT-VEGETATION products , 2014 .

[16]  Stanford B. Hooker,et al.  An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series , 2004 .

[17]  Xiaoyang Zhang,et al.  Interannual variations in spring phenology and their response to climate change across the Tibetan Plateau from 1982 to 2013 , 2016, International Journal of Biometeorology.

[18]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[19]  Guirui Yu,et al.  Diurnal, seasonal and annual variation in net ecosystem CO2 exchange of an alpine shrubland on Qinghai‐Tibetan plateau , 2006 .

[20]  Qinchuan Xin,et al.  A risk-benefit model to simulate vegetation spring onset in response to multi-decadal climate variability: Theoretical basis and applications from the field to the Northern Hemisphere , 2016 .

[21]  Z. Niu,et al.  Watershed Allied Telemetry Experimental Research , 2009 .

[22]  Combined effects of warming, snowmelt timing, and soil disturbance on vegetative development in a grassland community , 2014, Plant Ecology.

[23]  Yanhong Tang,et al.  Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau , 2011 .

[24]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[25]  R. Giering,et al.  Carbon cycle data assimilation with a generic phenology model , 2010 .

[26]  Hasbagan Ganjurjav,et al.  Effects of Warming on CO2 Fluxes in an Alpine Meadow Ecosystem on the Central Qinghai–Tibetan Plateau , 2015, PloS one.

[27]  Dailiang Peng,et al.  Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites , 2017 .

[28]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[29]  V. Salomonson.  The Moderate Resolution Imaging Spectrometer (MODIS) , 1990 .

[30]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[31]  José A. Sobrino,et al.  Accelerated Changes of Environmental Conditions on the Tibetan Plateau Caused by Climate Change , 2011 .

[32]  José A. Sobrino,et al.  Global land surface phenology trends from GIMMS database , 2009 .

[33]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[34]  S. Goetz,et al.  Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment , 2014, Global change biology.

[35]  T. Andrew Black,et al.  Comparison of regional carbon flux estimates from CO2 concentration measurements and remote sensing based footprint integration , 2008 .

[36]  Nicholas C. Coops,et al.  Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand , 2007 .

[37]  Youngwook Kim,et al.  Climatic Controls on Spring Onset of the Tibetan Plateau Grasslands from 1982 to 2008 , 2015, Remote. Sens..

[38]  Chang-Hoi Ho,et al.  Increase in vegetation greenness and decrease in springtime warming over east Asia , 2009 .

[39]  P. Ciais,et al.  Influence of spring and autumn phenological transitions on forest ecosystem productivity , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[40]  Josep Peñuelas,et al.  Phenology Feedbacks on Climate Change , 2009, Science.

[41]  Compton J. Tucker,et al.  A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series , 2014, Remote. Sens..

[42]  A. Arneth,et al.  Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation , 2010 .

[43]  J. Peñuelas,et al.  Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations , 2016 .

[44]  Rik Leemans,et al.  Faculty Opinions recommendation of European phenological response to climate change matches the warming pattern. , 2006 .

[45]  Shuhua Yi,et al.  Increasing contamination might have delayed spring phenology on the Tibetan Plateau , 2011, Proceedings of the National Academy of Sciences.

[46]  K. Thonicke,et al.  Identifying environmental controls on vegetation greenness phenology through model–data integration , 2014 .

[47]  Zhixiang Xiao,et al.  Does the climate warming hiatus exist over the Tibetan Plateau? , 2015, Scientific Reports.

[48]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[49]  Tao Wang,et al.  Declining snow cover may affect spring phenological trend on the Tibetan Plateau , 2013, Proceedings of the National Academy of Sciences.

[50]  Guirui Yu,et al.  Net ecosystem CO2 exchange and controlling factors in a steppe—Kobresia meadow on the Tibetan Plateau , 2006 .

[51]  P. Gong,et al.  Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics , 2015 .

[52]  Yu Qin,et al.  Actual Evapotranspiration in Suli Alpine Meadow in Northeastern Edge of Qinghai-Tibet Plateau, China , 2015 .

[53]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[54]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[55]  Mingguo Ma,et al.  Validation of MODIS-GPP product at 10 flux sites in northern China , 2013 .

[56]  Shilong Piao,et al.  No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade , 2013, Proceedings of the National Academy of Sciences.

[57]  Mingjun Ding,et al.  Spatio-temporal variation of spring phenology in Tibetan Plateau and its linkage to climate change from 1982 to 2012 , 2016, Journal of Mountain Science.

[58]  Xinquan Zhao,et al.  Seasonal and inter-annual variations in CO2 fluxes over 10 years in an alpine shrubland on the Qinghai-Tibetan Plateau, China , 2016 .

[59]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[60]  Shilong Piao,et al.  Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau , 2014 .

[61]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[62]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[63]  张. Z. Juan,et al.  Phenological variation of alpine grasses (Gramineae) in the northeastern Qinghai-Tibetan Plateau, China during the last 20 years , 2014 .

[64]  Yu Zhang,et al.  Biophysical regulation of carbon fluxes over an alpine meadow ecosystem in the eastern Tibetan Plateau , 2014, International Journal of Biometeorology.

[65]  H. Schmid,et al.  A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP) , 2015 .

[66]  Mark A. Liniger,et al.  A global reanalysis of vegetation phenology , 2011 .