Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach
Introduction
Evapotranspiration (ET) is a nexus of energy, water, and the carbon cycle (Yang et al., 2016) which provides the interactions between climatic and hydrological processes to address the changes among plant-land-atmosphere conditions (Miralles et al., 2016). Actual evapotranspiration (AET) is defined as the loss of water from the surface of the Earth to the atmosphere, and it is typically linked with evaporation from the soil surface and plant transpiration in its natural environment under limited water conditions (Brutsaert and Chen, 1996). Availability of accurate and reliable AET information is a prerequisite for many hydro-meteorological applications such as water resource management, irrigation scheduling, crop yield estimation, and drought predictions (Liaqat et al., 2015; Baik and Choi, 2015). However, the involvement of sensitive climate feedback, heterogeneous land surfaces and environmental conditions, and their variability in space and time usually cause small to large-scale quantitative and qualitative uncertainties in the AET quantification (Ferguson et al., 2010; Long et al., 2014).
Despite of the above quantification and uncertainty challenges, the local field scale AET datasets generated through conventional ground-based techniques such as, scintillometers, weighing lysimeters, Bowen ratio, and eddy covariance based FLUXNET systems are usually considered as true representations of AET at a point scale, inspite of their own measurement errors and scaling issues (Park et al., 2017a,b; Sugita et al., 2017; Velpuri et al., 2013; Baik and Choi, 2015). Continuous efforts have been made in last few decades to produce multi-year global AET datasets by combining conventional methods with recent advancements in satellite remote sensing technologies (Rodell et al., 2004; Miralles et al., 2011a; Ghilain and Arboleda, 2011; Zhang et al., 2010; Onogi et al., 2007; Mu et al., 2007, 2011). These datasets have been generated considering the widespread application of the surface energy budget, land surface models, and empirical as well as semi-empirical approaches through the use of satellite imagery and observation based meteorological forcing (Landeras et al., 2008; Miralles et al., 2011a).
Among several global AET products (Rodell et al., 2004; Mu et al., 2007, 2011; Miralles et al., 2011a; Zhang et al., 2010; Ghilain and Arboleda, 2011; Onogi et al., 2007), very few qualify as datasets, which are widely available possess large coverage and find operational applications. Examples of such datasets include the MODerate resolution Imaging Spectroradiometer (MODIS) i.e., MOD16 AET (Mu et al., 2007, 2011) having eight-day, monthly, and yearly temporal and 1 km spatial resolution; Global Land Evaporation and Amsterdam Model (GLEAM) 25 km daily AET products (Martens et al., 2017; Miralles et al., 2011a,b); and Global Land Data Assimilation System (GLDAS) having 25 km AET products at 3 hourly and monthly temporal resolutions (Rodell et al., 2004). Moreover, MOD16, GLEAM, and GLDAS provide continuous AET datasets on a global scale which are based on Penman-Monteith, Priestley-Taylor, and land surface model approaches, respectively. All of these datasets are based on geophysical measurement systems such as models estimation, remote sensing and in-situ network forcing which are subject to various error sources that include the difference between in-situ measurements itself, sensor calibration and support scale, and underlying model assumptions and parametrization. Thus, the uncertainty analysis of these error sources is critical to characterize and use the above AET datasets with greater confidence in operational applications involving hydro-meteorological projections.
Currently, two types of error estimation techniques are available to investigate the accuracy of geophysical measurements. The first method involves measurments of the difference between modeled and in-situ datasets in terms of correlation, bias, and root mean square error (Majozi et al., 2017; Velpuri et al., 2013; Kim et al., 2012; Liu et al., 2016; Ramoelo et al., 2014; Li et al., 2014). This method is mostly used for calibration and validation purposes due to limited in-situ datasets availability (Jia et al., 2012). The second method employs the triple collocation (TC) error estimation technique (Stoffelen, 1998) that uses the statistical relationships to estimate the random error standard deviation for three collocated datasets of the same geophysical variable. At the same time, it does not consider any dataset which belongs to the perfectly observed system. This implies that the errors associated with target dataset are a true representation of uncertainty associated with that system, and this is mutually independent of the other two sources. Moreover, the mathematical simplicity of covariance based extended TC analysis is that the high-quality reference datasets are not really required. Instead the TC method directly estimates the Pearson’s correlation coefficient between noisy and unknown reference datasets (McColl et al., 2014; Gruber et al., 2016). Therefore, TC has emerged as one of the most promising error estimation techniques for earth observations and hydrological applications due to its optimal utilization of an independent reference system. Although, this method has been widely used to map error structures of a number of geophysical variables including ocean, wind and wave data (Caires and Sterl, 2003), precipitation (Alemohammad et al., 2015), sea surface temperature (Gentemann, 2014), leaf area indices (Fang et al., 2012) and more specifically soil moisture (Kim et al., 2018; Gruber et al., 2016; Scipal et al., 2008; Su et al., 2014a,b), but, to best of our knowledge, it has not yet been considered for analyzing AET datasets.
Therefore, the main objective of this study is to examine the error structure of three independently available long-term AET (MOD16, GLEAM, and GLDAS) datasets over a 10-years duration (2000–2010) in Asia using new covariance based extended TC method. The accuracy of each AET dataset was first examined by comparing its results independently against nine different eddy covariance-based flux tower observations collected over a range of rice paddy, forest, and grassland ecosystems. The results of error structures obtained using the TC method were then interpreted to understand the uncertainties and usage of each AET dataset under their observed dynamics. Moreover, we have also examined the spatio-temporal variations of TC error structures corresponding to land use land cover types in order to choose a suitable AET product for different land surface ecosystems in the region. Overall, the analysis of error characteristics in this study represents a significant step towards the evaluation of multiple global AET products under different land use conditions, which may serve as a baseline for suitable data assimilation insights.
Section snippets
Study area
This study was conducted within the extent of Asia (Fig. 1) the Wolrd’s largest and most populous continent with a heterogeneous land cover and fragile ecosystem. It was considered that the selected study region would likely experience accelerated hydrological cycles due to the recent increase in temperature and global warming effects (Zohaib et al., 2017; Liaqat and Choi, 2015; Byun et al., 2014). Recent modeling endeavors indicate that such warming effects would be prominent in the Tibetan
Extended triple collocation (TC) approach
The extended triple collocation (TC) approach was used to investigate the error structure of three globally available (MOD16, GLEAM, and GLDAS) AET datasets. Extended TC uses a statistical technique among three spatially coincident and temporally collocated datasets of the same geophysical variable to derive standard deviation (Std) of random error (), simultaneously (McColl et al., 2014; Stoffelen, 1998; Gruber et al., 2016; Su et al., 2014a,b). The main advantage of the use of extended TC
Physical uncertainty analysis of AET products using flux tower point measurements
The time series and scatter plot analysis presented in Fig. 2 reveal the seasonal patterns predicted by the three AET (MOD16, GLEAM, and GLDAS) datasets over 8-day timescale in comparison to those obtained from the nine eddy-covariance based flux tower measurements collected over varying topography and a range of mixed forest (Fig. 2a), rice paddy (Fig. 2b), grassland and alpine meadow ecosystems (Fig. 2c). These comparison depicted that all the three AET products have relatively well captured
Conclusion
This study characterizes the quality of MOD16, GLEAM, and GLDAS widely available AET products based on their physical and theoretical uncertainties across several land cover systems within the extent of South and East Asia during the period of 2000–2010. In terms of physical accuracy analysis, the point estimations obtained from three (MOD16, GLEAM, and GLDAS) products shows moderate to good correlation (R) with corresponding average value of (0.35–0.60, 0.63–0.84 and 0.48–0.79) compared to
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2016R1A2B4008312). This research was supported by Space Core Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2014M1A3A3A02034789).
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