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1. Introduction
The Global Land Data Assimilation System (GLDAS) is a global offline (uncoupled to the atmosphere) terrestrial modeling system (Rodell et al. 2004), developed jointly by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP). Two versions of GLDAS datasets are available online: GLDAS, version 1 (GLDAS-1), which drives four land surface models (LSMs)-Noah, the Community Land Model (CLM), the Variable Infiltration Capacity model (VIC), and Mosaic-and GLDAS, version 2 (GLDAS-2), which drives only the Noah model so far. GLDAS provides a unique opportunity for the geoscience community to assess the global and regional environment change at up to 0.25° spatial resolution and 3-hourly temporal resolution. The fields of land surface water states and fluxes provided by GLDAS include rainfall rate, snowfall rate, evapotranspiration (ET), soil moisture in different layers, surface runoff, and subsurface runoff.
GLDAS data products have been used for assessing changes of terrestrial water storage (TWS; e.g., Yang et al. 2013; Huang et al. 2013; Proulx et al. 2013; Yang and Chen 2015), identifying global dryland areas (Ghazanfari et al. 2013), drought monitoring (Hao et al. 2014), and long-term soil moisture changes (Zawadzki and Kȩdzior 2014). Syed et al. (2008) showed that GRACE-based storage changes are in agreement with those obtained from GLDAS simulations. Rodell et al. (2007) used GLDAS outputs (soil moisture and snow water equivalent) as auxiliary information to isolate groundwater storage anomalies from GRACE TWS. Meanwhile, GLDAS data have been used by some authors (e.g., Gao et al. 2014) to assess long-term land surface change without full awareness of the issue of data quality, which may lead to false detection of changes resulting from data problems.
Many studies have been conducted for validating the GLDAS-1 data products. Zaitchik et al. (2010) found that the four LSMs included in GLDAS-1 yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. Wang et al. (2011) showed that the 0.25° × 0.25° GLDAS-1/Noah daily and monthly precipitation data are of high quality for a mesoscale basin in northeastern China. Wang and Zeng (2012) evaluated six reanalysis products (i.e., MERRA, NCEP-NCAR,...