# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 2 Materials and Method
### Method
#### 2.3.2 Temporal and Spatial Trend Analysis of WUE, GPP, and ET  
In order to elucidate the temporal and spatial variations in WUE, GPP, and ET within the drylands of China, this study employed Theil-Sen (Sen) trend analysis and the Mann-Kendall (MK) test. These methodologies exhibit robust capabilities in mitigating the impact of outliers and measurement errors. The formula for calculating the _slope_ is as follows:  
\[slope=\text{Median}\left[\frac{x_{j}-x_{i}}{j-i}\right],1\leq i\leq j\leq n \tag{2}\]  
Median is the median function. \(n\) is the number of years. There is a total of 20 years from 2001 to 2020; \(i\) and \(j\) are time series numbers; \(x_{i}\) and \(x_{j}\) are the values corresponding to the variable x in the i-th and j-th years respectively. A _slope_ greater than 0 indicates an increasing trend in WUE, whereas a _slope_ less than 0 signifies a decreasing trend. The significance of trends is evaluated at a 95% confidence level, corresponding to a significance level where the \(|Z|\) statistic is greater than 1.96. Based on the outcomes of trend analysis and significance testing, the patterns of WUE variation in this study were categorized into four distinct modes: significantly increasing (_slope_ > 0, \(|Z|\) > 1.96), insignificantly increasing (_slope_ > 0, \(|Z|\) < 1.96), significantly decreasing (_slope_ < 0, \(|Z|\) > 1.96), and insignificantly decreasing (_slope_ < 0, \(|Z|\) < 1.96). Especially in water-scarce regions, there exists a complex equilibrium relationship among WUE, GPP, and ET. Vegetation degradation leads to reduced GPP. Initially, plants \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Source** & **Product** & **Resolution** & **Time Series** \\ \hline Meteorological data & China Meteorological Data Service Center & V3 Datest & 1 day & 2001–2020 \\  & Google Earth Engine (GEE) & NASA LP DAAC & 500 m & 2001–2020 \\  & ([[https://earthenengine.google.com](https://earthenengine.google.com)]([https://earthenengine.google.com](https://earthenengine.google.com))) & MOD17A2H & 8 days & 2001–2020 \\  & Google Earth Engine (GEE) & NASA LP DAAC & 500 m & 2001–2020 \\  & ([[https://earthenengine.google.com](https://earthenengine.google.com)]([https://earthenengine.google.com](https://earthenengine.google.com))) & MOD16A2 & 8 days & 2001–2020 \\  & Google Earth Engine (GEE) & NASA LP DAAC & 500 m & 2001–2020 \\  & ([[https://earthenengine.google.com](https://earthenengine.google.com)]([https://earthenengine.google.com](https://earthenengine.google.com))) & MOD15A2H & 8 days & 2001–2020 \\  & & Global Climate & & \\ SWC & European Centre for Medium-Range Weather Forecasts & Atmospheric & 0.1\({}^{\circ}\) & \\  & (ECMWF) ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/))) & Reanalysis & 1 month & 2001–2020 \\  & (ERA5) & & & \\ Land Cover & Zenodo ([[https://zenodo.org/](https://zenodo.org/)]([https://zenodo.org/](https://zenodo.org/))) & CLCD (Yang and Huang, 2021) & 30 m & 2001–2020 \\  & & Huang, 2021) & 1 year & 2001 \\  & Vegetation type & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & & 1 km & (Publication time) \\  & Soil type (Soilt) & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & & 1 km & 1995 \\  & & & & 2009 \\  & Geomorphic type & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & & 1 km & (Publication time) \\  & (Geomt) & SRTMDEMUTM & 90 m & \multicolumn{1}{c}{—} \\  & & & & \\  & (Elev) & Geospatial Data Cloud ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn))) & SRTMDEMUTM & 90 m & \multicolumn{1}{c}{—} \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data sources. may close stomata, decreasing ET and increasing WUE. Later, increased surface bareness reduces WUE [39].