# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 2 Materials and Methods
### Data Acquisition and Processing  
This study uses GPP (MOD17A2) and ET (MOD16A2) to calculate WUE with a spatial resolution of 500 m and a temporal resolution of 8 days. The time span was from 2001 to 2021, and data from April-October of each year were selected. MOD17A2H Version 6 (GPP) and MOD16A2 Version 6 (ET) are the accumulated values of the eight-day composite cycle. Urban and built-up lands, water bodies, snow and ice covered areas and desert areas are almost devoid of vegetation and these areas lack GPP and ET data, so these areas are masked.  
The driver data in this study included atmospheric factors (TEM, RN, LST, VPD, DEF, VAP, AOD, TSVEG), hydrological factors (PRE, VSW1, GWS, RTZSM, SFSM, PDSI, RO, SW), and biological factors (CRSI, LAI, EVI, FVC). They are expressed using the average of April-October data for each year from 2001 to 2021. Specifically, TEM (surface 2-m temperature), PRE (precipitation accumulation), and SW (soil water (0-7 cm)) data were extracted from ERA5, RN (net radiation) data were calculated from longwave net radiation, and shortwave net radiation was extracted from ERA5. AOD (550 nm thickness) was extracted from MCD19A2. VPD, water pressure (VAP), climatic water deficit (DEF), runoff (RO), soil moisture (SW), and drought index (PDSI) were obtained from TERRACILMATE. Transpiration (TSVEG) was obtained from NOAH. LST was obtained from MOD11A2. Enhanced Vegetation Index (EVI) was obtained from MOD13Q1. FVC was calculated from Normalized Difference Vegetation Index (NDVI) data for MOD13Q1. The canopy salinity index (CRSI) was calculated from MOD09A1 data [41]. We used the groundwater content, soil moisture in the root zone (RTZSM), and surface soil moisture (SFSM) to characterize the distribution of groundwater. Specific data are detailed in Table 1.  
Figure 2: Overview of the study area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **Data** & **Type** & **Data Description** & **Spatial Resolution** & **Time Resolution** & **Data Source** \\ \hline
1 & GPP & & Gross primary & & & Google Earth Engine \\  & & productivity (bgC-m\({}^{-2}\)-8 & 500 m & 8 days & & LP DAAC\(\sim\)MOD17A2H (urgs-gov) \\  & & day\({}^{-1}\)) & & & & [[https://pddac.usgs.gov/products/mod17a2](https://pddac.usgs.gov/products/mod17a2) hv006/]([https://pddac.usgs.gov/products/mod17a2](https://pddac.usgs.gov/products/mod17a2) hv006/), & & accessed on 13 December 2022 \\ \hline
2 & ET & & Total evapotranspiration & & Google Earth Engine \\  & & & (bg C m\({}^{-2}\)-8 day\({}^{-1}\)) & 500 m & 8 days & & LP DAAC\(\sim\)MOD16a2 (urgs-gov) \\  & & & & & [[https://pddac.usgs.gov/products/mod16a2v006/](https://pddac.usgs.gov/products/mod16a2v006/)]([https://pddac.usgs.gov/products/mod16a2v006/](https://pddac.usgs.gov/products/mod16a2v006/)), & & \\  & & & & & accessed on 13 December 2022 \\ \hline X1 & TEM & Atmospheric factor & Temperature\_2m (\(\lambda\)) & 11,132 m & Monthly & & Google Earth Engine \\  & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & & \\ X2 & PRE & Hydrological factor & Total precipitation (m) & 11,132 m & Monthly & & \\  & & Resultant of the surface & & & & & \\ X3 & RN & Atmospheric factor & net solar and thermal & 11,132 m & Monthly & & \\  & & radiation data (\(\rm{/m^{2}}\)) & & & & \\  & & & Volume: soil water & & & & \\ X4 & VSW1 & Hydrological factor & current (\(\rm{0^{-7}}\) cm depth) & 11,132 m & Monthly & & \\  & & & (\(\rm{m^{2}/m^{2}}\)) & & & & \\ \hline X5 & CRSI & Biological factor &
\begin{tabular}{c} Canopy Response \\ Salinity Index \\ \(\sqrt{\frac{\  
\begin{table}
\begin{tabular}{c c c c c c c} \hline