## 2 Materials and Methods
### Delineation of Drought Region  
The standard precipitation index (SPI) for drought [39] was used to determine the drought events in the study area. SPI was calculated by fitting historical precipitation data to a Gamma probability distribution for the study time period and geographic area, and then transforming the Gamma function to a normal distribution [39]. The SPI has become an important tool for assessing moisture conditions based on monthly total precipitation. The remote sensing-based drought indices are mainly focused on using vegetation indices. Compared to remote-sensing data, meteorological data from weather stations generally cover longer periods of time and are more precise. As a result, SPI was selected in this study for its simplicity, temporal flexibility and spatial consistency. The 3-month SPI values were calculated using monthly precipitation data from 1972 to 2012. In our study, values of 3-month SPI between \(-0.99\) and \(0.99\) denote average conditions (baseline conditions) and absolute values greater than 2.0 represent extreme conditions. The index classes (\(-1.99\) to \(-1.50\)) and (\(-1.49\) to \(-1.0\)) represent severe drought and moderate drought. On the basis of monthly 3-month SPI datasets, we calculated the mean SPI and minimum SPI during drought periods (Sep. 2009 to Mar. 2010) (Figure 2). The soil moisture dataset from remote-sensing data was used as a proxy for soil water conditions in southern China. The global soil moisture dataset has been generated by active and passive microwave spaceborne instruments and covers the 32-year period from 1978 to 2010 ([[http://www.esa-soilmoisture-cci.org](http://www.esa-soilmoisture-cci.org)]([http://www.esa-soilmoisture-cci.org](http://www.esa-soilmoisture-cci.org))) at 0.25\({}^{\circ}\) resolution. The active data set was generated by the University of Vienna (TU Wien) based on obsesrvations from the C-band scatter meters on board the European Remote Sensing satellites (ERS-1/2) and Meteorological Operational satellite program (METOP-A). The passive data set was generated by the VU University Amsterdam in collaboration with the National Aeronautics and Space Administration (NASA) based on passive microwave observations from Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager of the Defense Meteorological Satellite Program (DMSP SSM/I), the microwave imager from the Tropical Rainfall Measuring Mission microwave imager (TRMM TMI) and the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) onboard the Aqua satellite. Due to a failure of the input sensors (ERS 2) in 2002-2005, the continuous soil moisture data from 2006 to 2010 was used in this study. The soil moisture dataset overlapped by the boundary of south-western China was used to detect changes in soil water condition of the forest ecosystem imposed by droughts during 2009-2010 in south-western China. Figure 1: The location of south-western China’s forests. Five provinces in south-western China experienced the 2009–2010 drought events. Ecosystem Measures of Net Primary Productivity (NPP), Evaportranspiration (ET) and Water-Use Efficiency (WUE)  
The ecosystem functional indicators, i.e., NPP, ET, and WUE were used to explore the effects of carbon and the water cycle of the forest ecosystem in south-western China to 2009/2010 droughts events. Daily NPP, ET and WUE at a 1 km resolution for south-western China were derived from the national outputs of the BEPS (Boreal Ecosystem Productivity Simulator) model [40; 41] and were summed to monthly scale from 2001 to 2010. These datasets were overlaid with a forest map of south-western China (Figure 1) at a 1 km resolution. We estimated monthly WUE using NPP (gC/m\({}^{2}\)/month) and ET (mm/month), which links carbon and water cycles that is a key index of ecosystem function [42], and reflects \"trade-off\" relationship between carbon assimilation and water loss of terrestrial ecosystems [43].