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Article Is Available Online Land Surface Evaporation by Four Schemes and Comparison with at Doi:10.5194/Acp-14-13097-2014-Supplement Atmos. Chem. Phys., 14, 13097–13117, 2014 www.atmos-chem-phys.net/14/13097/2014/ doi:10.5194/acp-14-13097-2014 © Author(s) 2014. CC Attribution 3.0 License. Development of a 10-year (2001–2010) 0.1◦ data set of land-surface energy balance for mainland China X. Chen1, Z. Su1, Y. Ma2, S. Liu3, Q. Yu4, and Z. Xu3 1Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands 2Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, CAS Center for Excellence and Innovation in Tibetan Plateau Earth Sciences, Beijing, China 3State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China 4Plant Functional Biology & Climate Change Cluster, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia Correspondence to: X. Chen ([email protected]) Received: 20 February 2014 – Published in Atmos. Chem. Phys. Discuss.: 5 June 2014 Revised: 15 September 2014 – Accepted: 17 October 2014 – Published: 10 December 2014 Abstract. In the absence of high-resolution estimates of the download for studies of the water cycle and environmental components of surface energy balance for China, we devel- change in China. oped an algorithm based on the surface energy balance sys- tem (SEBS) to generate a data set of land-surface energy and water fluxes on a monthly timescale from 2001 to 2010 at a 0.1 × 0.1◦ spatial resolution by using multi-satellite and me- 1 Introduction teorological forcing data. A remote-sensing-based method was developed to estimate canopy height, which was used As China is one of the fastest growing and urbanizing to calculate roughness length and flux dynamics. The land- economies in the world, changes in land cover and land use surface flux data set was validated against “ground-truth” can significantly influence the environment by altering land– observations from 11 flux tower stations in China. The esti- atmosphere energy and water exchanges (Suh and Lee, 2004; mated fluxes correlate well with the stations’ measurements Lin et al., 2009). For instance, rapid urban expansion has for different vegetation types and climatic conditions (aver- substantially changed land-surface heat fluxes in the Pearl age bias D 11.2 Wm−2, RMSE D 22.7 Wm−2). The quality River delta (PRD) (Lin et al., 2009), and has increased sensi- of the data product was also assessed against the GLDAS ble heat fluxes in the Beijing metropolitan area (C. Zhang et data set. The results show that our method is efficient for al., 2009). The variability of surface energy balance and its producing a high-resolution data set of surface energy flux partitioning may also have an important impact on climate for the Chinese landmass from satellite data. The validation variability in China (Sun and Wu, 2001). Similarly, changes results demonstrate that more accurate downward long-wave in surface energy fluxes have been shown to alter the inten- radiation data sets are needed to be able to estimate turbulent sity of the East Asian monsoon (Zhou and Huang, 2008; Qiu, fluxes and evapotranspiration accurately when using the sur- 2013; Hsu and Liu, 2003). In short, understanding variation face energy balance model. Trend analysis of land-surface ra- in energy fluxes is important for the study of climate change diation and energy exchange fluxes revealed that the Tibetan in China (Brauman et al., 2007). Nevertheless, the spatial and Plateau has undergone relatively stronger climatic change temporal variability of China’s land-surface energy balance, than other parts of China during the last 10 years. The ca- and the magnitude of each, are still unknown. pability of the data set to provide spatial and temporal in- While it is of critical importance to understand the parti- formation on water-cycle and land–atmosphere interactions tioning of water and energy distribution across China’s ter- for the Chinese landmass is examined. The product is free to restrial surface, accurate monitoring of its spatial and tempo- ral variation is notoriously difficult (Ma et al., 2011). Several Published by Copernicus Publications on behalf of the European Geosciences Union. 13098 X. Chen et al.: Development of 10 year land surface fluxes for China field experiments are being carried out to monitor turbulent heat flux data set (Jiménez et al., 2009) for the Chinese land- fluxes over selected land cover in China by using ground- mass. The use of remotely sensed data offers the potential based eddy covariance devices (Wang et al., 2010; Yu et al., for acquiring observations of variables such as albedo, land- 2006; Y. Ma et al., 2008; Li et al., 2009). However, these surface temperature, and normalized difference vegetation measurements are only representative of small areas around index (NDVI) on a continental scale for China. Is it possi- the locations where the measurements are being made. For ble to use all available satellite-observed land-surface vari- this reason, the establishment of an eddy-covariance flux net- ables directly to calculate high-resolution land-surface fluxes work cannot provide a complete land-surface heat flux pic- for the Chinese landmass, due to the reanalysis data hav- ture for the entire Chinese landmass. ing a coarse spatial resolution and containing large uncer- A number of methods can be used to derive land-surface tainty? Since surface fluxes cannot be detected directly by energy fluxes. Jung et al. (2009), for example, generated satellite-borne sensors, an alternative for estimating conti- global spatial flux fields by using a network upscaling nental water and energy fluxes can be derived by applying method. However, their flux network included only a lim- the aerodynamic theory of turbulent flux transfer (Ma et al., ited number of flux stations in China. The Global Soil Wet- 2011) or by establishing statistical relationships between re- ness Project 2 (GSWP-2) (Dirmeyer et al., 2006) produced lated satellite observations and land-surface fluxes (Jiménez a global land-surface product on a 1 × 1◦ grid for the period et al., 2009; Wang et al., 2007). Most remotely sensed la- 1986 to 1995. The Global Land Data Assimilation System tent heat flux or evapotranspiration products have null val- (GLDAS) (Rodell et al., 2004) can provide a global cover- ues in urban, water, snow, barren and desert areas (Mu et al., age in the form of 3-hourly, 0.25◦ data. Furthermore, prod- 2007; Wang et al., 2007; Jiménez et al., 2009). This is due ucts from the European Centre for Medium-Range Weather to the lack of a uniform representation of turbulent exchange Forecasts (ECMWF) interim reanalysis (ERA-Interim) (Dee processes over different types of land cover in their method. et al., 2011), the National Centers for Environmental Predic- Meanwhile, the aerodynamic turbulent transfer method can tion (NCEP) (Kalnay et al., 1996), Modern-Era Retrospec- describe the flux exchange through changes in surface rough- tive Analysis for Research and Applications (MERRA) (Rie- ness length over different land covers. Statistical methods necker et al., 2011) and other reanalysis data can also provide establish relationships between satellite-sensed observations temporally continuous – but coarse – spatial resolution data (e.g., NDVI, LST, albedo) and land-surface fluxes through sets of land-surface fluxes. Jiménez et al. (2011) made an various fitting techniques (Wang et al., 2007). The simple inter-comparison of different land-surface heat flux products. relationships established cannot give a reasonable approx- When these products were applied on continental scales, the imation for extreme conditions such as bare soil or other different approaches resulted in large differences (Vinukollu types of non-canopy land cover (e.g., lakes, deserts), because et al., 2011a; Jiménez et al., 2011; Mueller et al., 2011). land covers behave significantly differently in land-surface The problems met by using currently available flux data energy flux partitioning. Fortunately, turbulent flux transfer in climate studies of China have been reported by Zhou and parameterization can overcome the shortcomings of statisti- Huang (2010). Zhu et al. (2012) have also reported that sum- cal methods and produce spatially continuous distributions mer sensible heat fluxes derived from eight data sets (includ- of land-surface energy fluxes with prepared meteorological ing NCEP, ERA, and GLDAS) of China’s Tibetan Plateau forcing data. For this reason, we chose a more physically region differ from each other in their spatial distribution. In based method – turbulent flux parameterization – to produce addition, all the flux data sets mentioned above are based the data set. on model simulations, which have deficiencies in studying The challenge in using turbulent flux parameterization changes in water-cycle and land–air interactions in China lies in the transition from regional to continental and global (Y. Chen et al., 2013; Su et al., 2013; Wang and Zeng, 2012; scales, because meteorological data of high resolution (i.e., L. Ma et al., 2008). 1–10 km) are not easily obtained for a large region. Recently, A spatially and temporally explicit estimate of surface en- Chinese scientists produced high-resolution meteorological ergy fluxes is of considerable interest for hydrological as- forcing data that can be used in our study. Another issue is sessments and meteorological and climatological investiga- the complexity met by the method when combining different tions (Norman et al., 2003). Satellite-sensed data of surface spatial and temporal sampling input variables. This is dis- variables can be used to produce maps of heat and water cussed in detail in Sect. 3.1. The last difficulty that has sur- fluxes on different scales (Wang and Liang, 2008; X. Li et rounded the application of turbulent flux parameterization on al., 2012; Liu et al., 2010; Vinukollu et al., 2011b).
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