# Diverse Roles of Previous Years' Water Conditions in Gross Primary Productivity in China
## 2 Materials and Methods
### Research Data and Processing
#### 2.2.2 Meteorological Data  
In this study, we used monthly meteorological data from the Climatic Research Unit (CRU) at the University of East Anglia for the period from 1901 to 2016 ([[http://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.01/](http://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.01/)]([http://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.01/](http://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.01/))), including temperature (Temp), precipitation and potential evapotranspiration. The version of the dataset was TS 4.01, and the spatial resolution was 0.5\({}^{\circ}\). Monthly photosynthetic active radiation (PAR) was obtained from the Cloud and the Earth's Radiant Energy System (CERES) at NASA's Langley Research Center at a 1\({}^{\circ}\) spatial resolution for the period from 2000 to 2019 ([[https://ceres.larc.nasa.gov/order_data.php](https://ceres.larc.nasa.gov/order_data.php)]([https://ceres.larc.nasa.gov/order_data.php](https://ceres.larc.nasa.gov/order_data.php))). In this dataset, the total photosynthetic active radiation (PAR) was computed from the sum of the surface diffusive and direct PAR in the all-sky conditions [49].  
To match these spatial datasets, all spatial data were converted into the WGS84 coordinate system with the spatial resolution uniformly adjusted to 500 m in ArcGIS 10.1 through the nearest neighbour algorithm. At the same time, we detrended the climate factors of temperature, climate water deficit and photosynthetic active radiation by using a linear regression model to eliminate the potential impacts of change trends and to focus on the impacts of interannual variability. Specifically, we fitted the original climate data from 2001 to 2016 to get their linear trends and then minus their trends to get climate data without trends. The anomaly of climate water deficit (\(\text{CWD}_{\text{anomaly}}\)), which represents the difference between annual precipitation and annual potential evapotranspiration, was used to reflect the status of water conditions. We first calculated the climate water deficit (CWD) from 2001 to 2016 ([PERSON], 1998) and then defined 7 grades of water deficit (Table 1) based on the standard deviations (SDs) of the anomalies of the CWD series.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Water Conditions** & \(\text{CWD}_{\text{anomaly}}\) \\ \hline Severely wet & \(\text{CWD}_{\text{anomaly}}>2\) SD \\ Moderately wet & \(1.5\) SD \(<\text{CWD}_{\text{anomaly}}<2\) SD \\ Mildly wet & \(\text{SD}<\text{CWD}_{\text{anomaly}}<1.5\) SD \\ Normal & \(-\text{SD}<\text{CWD}_{\text{anomaly}}<\text{SD}\) \\ Mild drought & \(-1.5\) SD \(<\text{CWD}_{\text{anomaly}}<-\text{SD}\) \\ Moderate drought & \(-2\) SD \(<\text{CWD}_{\text{anomaly}}<-1.5\) SD \\ Severe drought & \(\text{CWD}_{\text{anomaly}}<-2\) SD \\ \hline \hline \end{tabular} SD: the standard deviation of the CWD series.  
\end{table}
Table 1: Seven grades of climate water deficit (\(\text{CWD}_{\text{anomaly}}\)).  
The dataset of monthly soil moisture for the period from 2000 to 2015 was derived from the Global Land Data Assimilation System (GLDAS) ([[http://ldas.gsfc.nasa.gov/gldas](http://ldas.gsfc.nasa.gov/gldas)]([http://ldas.gsfc.nasa.gov/gldas](http://ldas.gsfc.nasa.gov/gldas))); its spatial resolution was 0.25\({}^{\circ}\), and it included the soil moisture of four layers: 0-10 cm, 10-40 cm, 40-100 cm and 100-200 cm. We computed the annual mean soil moisture in 0-100 cm then resampled this data to 500 m for matching the climate water deficit data.