# Responses of Water Use Efficiency to Drought in Southwest China
## 2 Materials and Methods
### Datasets  
The data used in this study mainly include climate, remote sensing (GPP, ET, and land cover), drought index, and digital elevation model (DEM). The specific data sets and detailed information on the data are provided in Table 1.  
The monthly temperature and precipitation datasets for the period 2000-2017 were obtained from the China Meteorological Data Service Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). For each grid cell across Southwest China, the monthly station records were aggregated annually to calculate annual temperature and annual precipitation for each year over the study period. We also calculated the mean annual temperature (MAT) and mean annual precipitation (MAP). The land cover data with a spatial resolution of 1 km obtained from the Data Center of Resources and Environmental Sciences ([[http://www.resdc.cn/Default.aspx](http://www.resdc.cn/Default.aspx)]([http://www.resdc.cn/Default.aspx](http://www.resdc.cn/Default.aspx))) was used to identify the vegetation type of each grid cell. The four broad vegetation types examined in this study are forests, shrublands, grasslands, and croplands. The monthly MODIS GPP [31; 32] and ET [33; 34] data (with a 1-km resolution) during the period 2000-2017 were both obtained from University of Montana ([[http://www.ntsg.umt.edu](http://www.ntsg.umt.edu)]([http://www.ntsg.umt.edu](http://www.ntsg.umt.edu))). The reliability and accuracy of the GPP product (MOD17A2) have been evaluated by many previous studies [35; 36; 37; 38; 39], and it has been compared with ground-based data from various terrestrial ecosystems [32; 35]. The ET product (MOD16A2) is based on meteorological reanalysis data and vegetation attributes [34; 40], and has been evaluated with flux tower data from the United States [40; 41], Asia [42], and other regions. The global monthly Self-calibrating Palmer Drought Severity Index (scPDSI) with a 0.5\({}^{\circ}\)-spatial resolution was obtained from the Climatic Research Unit ([[https://crudata.uea.ac.uk/cru/data/drought/](https://crudata.uea.ac.uk/cru/data/drought/)]([https://crudata.uea.ac.uk/cru/data/drought/](https://crudata.uea.ac.uk/cru/data/drought/))). This drought index was used to analyze the drought conditions and the influences of drought on WUE during the period from 2000 to 2017. The scPDSI metric [30] was proposed based on the Palmer drought severity index (PDSI) [43], and the scPDSI is more comparable across different climate regimes than the traditional Palmer drought severity index (PSDI). The scPDSI was calculated from monthly precipitation and air temperature data at each location. The values of scPDSI range from \(-\)5.0 to 5.0, which indicates drought to humid conditions. Following [PERSON] et al. [44], the wet or dry conditions are divided into three categories: Drought (scPDSI \(\leq-\)1), normal (\(-\)1 \(<\) scPDSI \(<\) 1), and humid (1 \(\leq\) scPDSI), while the drought category can be further divided into slight drought, moderate drought, severe drought, and extreme drought (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Name**} & **Spatial Resolution** & **Temporal Resolution** & **Period** & **References** \\ \hline \multirow{2}{*}{Climate Data} & Temperature & 1 km & Monthly & 2000–2017 & China Meteorological Data \\  & Precipitation & 1 km & Monthly & 2000–2017 & Service Center \\ \hline \multirow{4}{*}{Geographic data} & DEM & 1 km & – & 2000 & SRTM 90m DEM Digital Elevation \\  & & & & & Database \\ \cline{2-6}  & \multirow{2}{*}{Land Cover} & \multirow{2}{*}{1 km} & \multirow{2}{*}{–} & \multirow{2}{*}{2010} & Data center of resources and \\  & & & & & environmental sciences, Chinese \\  & & & & & academy of sciences \\ \hline Remote sensing & GPP & 1 km & Monthly & 2000–2017 & MOD17A2 \\ products & ET & 1 km & Monthly & 2000–2017 & MOD16A2 \\ \hline Drought index & scPDSI & 0.5\({}^{\circ}\) & Monthly & 2000–2017 & Climatic Research Unit \\ \hline \hline \end{tabular}
\end{table}