# Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China
## Materials and Methods
### Data Sources and Processing  
#### 2.2.1 Climate Datasets  
The climate data sets used in this study were collected from the China Meteorological Data Service Center (CMDC, website: [[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). They are from 180 meteorological stations from across the YRB and cover the interval from 1961-2013. After removing stations with missing climatological and historical records (e.g., Banma, Guangshui, Folding and Pingwu), the number of stations decreased  
Figure 1: Location of the study area in China and (**a**) distribution of meteorological stations across the Yangtze River Basin (YRB); (**b**) topography of the YRB. The map projection system is World Geodetic System (WGS) 84.  
to 176; these stations provided high-quality and complete data sets for the study interval. We used spatial interpolation of the temperature and precipitation station to obtain a high-resolution climate change data set. The vegetation growing season in the YRB is from May to October [31]. 2.2 Simulations of Temperature and Precipitation Data Using World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP5) Multiple Climate Models  
The fifth phase of the Climate Model Intercomparison Project (CMIP5) was established by the World Climate Research Programme (WCRP) to support the Intergovernmental Panel on Climate Change (PICC's) assessment report. Although the CMIP5 model only provides analog output and has independent internal variability and initial conditions, and the observed synoptic model was different, the trends of variation between observed and simulated climate data provide useful information for achieving a better understanding of climate changes [32; 33]. The data used in this study are from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) database ([[https://pcmdi9.llnl.gov/search/cmip5/](https://pcmdi9.llnl.gov/search/cmip5/)]([https://pcmdi9.llnl.gov/search/cmip5/](https://pcmdi9.llnl.gov/search/cmip5/))). It comprises temperature and precipitation data from six models (CanESM2, CCSM4, INM-CM4, MIROC-ESM-CHEM, MPI-ESM-LR and MRI-CGCM3. See Table 1) of the CMIP5 model base for the YRB during 1961-2013 [21; 32; 34]. Based on the monthly temperature and precipitation data obtained from the six models, annual precipitation and temperature data were calculated and compared with the site meteorological data to examine the climatic trend of the YRB during 1961-2013 (Table 1). 2.3 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI)  
The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index product, MODIS 13A1, is a global vegetation dataset with a 500-m spatial resolution, and a 16-day composite temporal resolution ([[http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html)]([http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html))). The products from January 2001 to December 2013 were used in this study. The maximum value composites (MVC) were used to synthesize each 16-day data interval into monthly data, annual NDVI and annual EVI is averaged by the monthly NDVI and EVI data. The NDVI and EVI during the growing season (May-October) in the YRB during 2001-2013 were produced based on the average of the 6-month NDVI and EVI data. 2.4 Advanced Very High-Resolution Radiometer (AVHRR) at Boston University (BU) generated the Leaf Area Index (LAI)  
We used the third-generation Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI3g) from Advanced Very High-Resolution Radiometer (AVHRR) sensor products. These datasets have the following properties: temporal resolution of 15 days, and the spatial resolution of 1/12 for the period from 1982-2011 [35]. Monthly data were synthesized using the maximum synthesis method (MAC) from the original datasets.