Development of a 10-year ( 2001 – 2010 ) 0 . 1 ◦ data set of land-surface energy balance for mainland China

In the absence of high-resolution estimates of the components of surface energy balance for China, we developed an algorithm based on the surface energy balance system (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 meteorological forcing data. A remote-sensing-based method was developed to estimate canopy height, which was used to calculate roughness length and flux dynamics. The landsurface flux data set was validated against “ground-truth” observations from 11 flux tower stations in China. The estimated fluxes correlate well with the stations’ measurements for different vegetation types and climatic conditions (average bias = 11.2 Wm, RMSE = 22.7 Wm). The quality of the data product was also assessed against the GLDAS data set. The results show that our method is efficient for producing a high-resolution data set of surface energy flux for the Chinese landmass from satellite data. The validation results demonstrate that more accurate downward long-wave radiation data sets are needed to be able to estimate turbulent fluxes and evapotranspiration accurately when using the surface energy balance model. Trend analysis of land-surface radiation and energy exchange fluxes revealed that the Tibetan Plateau has undergone relatively stronger climatic change than other parts of China during the last 10 years. The capability of the data set to provide spatial and temporal information on water-cycle and land–atmosphere interactions for the Chinese landmass is examined. The product is free to download for studies of the water cycle and environmental change in China.

[1]  Shunlin Liang,et al.  A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data , 2013 .

[2]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[3]  Rong-hui Huang,et al.  An assessment of the quality of surface sensible heat flux derived from reanalysis data through comparison with station observations in Northwest China , 2010 .

[4]  Soroosh Sorooshian,et al.  Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments , 2005 .

[5]  Zhenghui Xie,et al.  Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach , 2013 .

[6]  Yu Zhang,et al.  An Improvement of Roughness Height Parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau , 2013 .

[7]  B. Bookhagen,et al.  Spatially variable response of Himalayan glaciers to climate change affected by debris cover , 2011 .

[8]  F. D. van der Meer,et al.  International institute for Geo - information Science and Earth Observation : ITC , 2009 .

[9]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[10]  H. Hsu,et al.  Relationship between the Tibetan Plateau heating and East Asian summer monsoon rainfall , 2003 .

[11]  Steven Platnick,et al.  Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data , 2004 .

[12]  Myoung-Seok Suh,et al.  Impacts of land use/cover changes on surface climate over east Asia for extreme climate cases using RegCM2 , 2004 .

[13]  K. Taniguchi,et al.  Seasonal variation of cloud activity and atmospheric profiles over the eastern part of the Tibetan Plateau , 2008 .

[14]  Ni Guo,et al.  Variability of soil moisture and its relationship with surface albedo and soil thermal parameters over the Loess Plateau , 2009 .

[15]  W. Massman,et al.  An Analytical one-Dimensional Second-Order Closure Model of Turbulence Statistics and the Lagrangian Time Scale Within and Above Plant Canopies of Arbitrary Structure , 1999 .

[16]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[17]  Gerald N. Flerchinger,et al.  Comparison of algorithms for incoming atmospheric long‐wave radiation , 2009 .

[18]  Shilong Piao,et al.  NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China , 2006 .

[19]  A. Betts,et al.  NCEP-NCAR and ECMWF Reanalysis Surface Water and Energy Budgets for the Mississippi River Basin , 2000 .

[20]  T. Vesala,et al.  Four-year (2006–2009) eddy covariance measurements of CO 2 flux over an urban area in Beijing , 2012 .

[21]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[22]  Yu Zhang,et al.  Estimation of turbulent fluxes using the flux-variance method over an alpine meadow surface in the eastern Tibetan Plateau , 2013, Advances in Atmospheric Sciences.

[23]  Xiangao Xia,et al.  Impacts of urban expansion and future green planting on summer precipitation in the Beijing metropolitan area , 2009 .

[24]  Z. Wan.  Collection-5 MODIS Land Surface Temperature Products Users' Guide , 2006 .

[25]  J. Timmermans,et al.  Coupling optical and thermal directional radiative transfer to biophysical processes in vegetated canopies , 2011 .

[26]  Yijian Zeng,et al.  Evaluation of ECMWF's soil moisture analyses using observations on the Tibetan Plateau , 2013 .

[27]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[28]  Hideki Kobayashi,et al.  Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales , 2011 .

[29]  W. Boos,et al.  Dominant control of the South Asian monsoon by orographic insulation versus plateau heating , 2010, Nature.

[30]  Jie He,et al.  Improving land surface temperature modeling for dry land of China , 2011 .

[31]  Shaomin Liu,et al.  Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China , 2013 .

[32]  Filipe Aires,et al.  Toward an estimation of global land surface heat fluxes from multisatellite observations , 2009 .

[33]  J. Qin,et al.  Evaluation of AMSR‐E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau , 2013 .

[34]  J. Norman,et al.  Remote sensing of surface energy fluxes at 101‐m pixel resolutions , 2003 .

[35]  Jun Qin,et al.  Can aerosol loading explain the solar dimming over the Tibetan Plateau? , 2012 .

[36]  X. Zeng,et al.  Comparison of land skin temperature from a land model, remote sensing, and in situ measurement , 2014 .

[37]  C. Kucharik,et al.  Role of Turbulent Heat Fluxes over Land in the Monsoon over East Asia , 2011 .

[38]  Weidong Guo,et al.  Observation analysis of land‐atmosphere interactions over the Loess Plateau of northwest China , 2010 .

[39]  Eric F. Wood,et al.  Multi‐model, multi‐sensor estimates of global evapotranspiration: climatology, uncertainties and trends , 2011 .

[40]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[41]  Shunlin Liang,et al.  An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[42]  William P. Kustas,et al.  An Evaluation of Two Models for Estimation of the Roughness Height for Heat Transfer between the Land Surface and the Atmosphere , 2001 .

[43]  Thian Yew Gan,et al.  Hydroclimatic trends and possible climatic warming in the Canadian Prairies , 1998 .

[44]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[45]  S. Running,et al.  A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006 , 2010 .

[46]  Ping Zhou,et al.  Spatiotemporal characteristics of dryness/wetness conditions across Qinghai Province, Northwest China , 2013 .

[47]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[48]  T. Manninen,et al.  CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo , 2012 .

[49]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[50]  Wilfried Brutsaert,et al.  Aspects of bulk atmospheric boundary layer similarity under free‐convective conditions , 1999 .

[51]  Jun Wen,et al.  Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau , 2009 .

[52]  Inge Sandholt,et al.  Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[53]  Shaomin Liu,et al.  Validation of remotely sensed evapotranspiration over the Hai River Basin, China , 2012 .

[54]  M. Bosilovich,et al.  Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations , 2012 .

[55]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[56]  Patrick Meir,et al.  Amazonian rain forests and drought: response and vulnerability. , 2010, The New phytologist.

[57]  T. A. Black,et al.  On the temporal upscaling of evapotranspiration from instantaneous remote sensing measurements to 8-day mean daily-sums , 2012 .

[58]  S. Seneviratne,et al.  Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations , 2011 .

[59]  D. Qin,et al.  Evaluation of ERA‐40, NCEP‐1, and NCEP‐2 reanalysis air temperatures with ground‐based measurements in China , 2008 .

[60]  Jie He,et al.  Solar radiation trend across China in recent decades: a revisit with quality-controlled data , 2010 .

[61]  J. Goudriaan,et al.  Crop Micrometeorology: A Simulation Study , 1977 .

[62]  Deliang Chen,et al.  Near‐surface air temperature lapse rates in the mainland China during 1962–2011 , 2013 .

[63]  Limin Yang,et al.  Quantification of land use/land cover changes in Pearl River Delta and its impact on regional climate in summer using numerical modeling , 2009 .

[64]  Dawen Yang,et al.  Seasonal and interannual variations in carbon dioxide exchange over a cropland in the North China Plain , 2009 .

[65]  Ying Li,et al.  Seasonal Changes of Turbulent Fluxes at a Typical Agricultural Site in the Chengdu Plain Based on Quality-Controlled Data , 2012 .

[66]  Weimin Ju,et al.  Evapotranspiration and water yield over China's landmass from 2000 to 2010 , 2013 .

[67]  S. Seneviratne,et al.  Global intercomparison of 12 land surface heat flux estimates , 2011 .

[68]  Z. Niu,et al.  Watershed Allied Telemetry Experimental Research , 2009 .

[69]  S. Liang,et al.  MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm , 2013 .

[70]  Zhanqing Li,et al.  A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature , 2007 .

[71]  Naota Hanasaki,et al.  GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface , 2006 .

[72]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[73]  T. Yao,et al.  Recent glacial retreat in High Asia in China and its impact on water resource in Northwest China , 2004 .

[74]  X. Lee,et al.  Overview of ChinaFLUX and evaluation of its eddy covariance measurement , 2006 .

[75]  Xubin Zeng,et al.  Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau , 2012 .

[76]  Xuelong Chen,et al.  Estimation of surface energy fluxes under complex terrain of Mt. Qomolangma over the Tibetan Plateau , 2012 .

[77]  A. Bondeau,et al.  Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model , 2009 .

[78]  Zhao-Liang Li,et al.  Radiance‐based validation of the V5 MODIS land‐surface temperature product , 2008 .

[79]  Guirui Yu,et al.  A preliminary study for spatial representiveness of flux observation at ChinaFLUX sites , 2006 .

[80]  Z. Su.  The Surface Energy Balance System ( SEBS ) for estimation of turbulent heat fluxes , 2002 .

[81]  Toshio Koike,et al.  Determination of regional net radiation and soil heat flux over a heterogeneous landscape of the Tibetan Plateau , 2002 .

[82]  Dawen Yang,et al.  Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain , 2010 .

[83]  Wenzhao Liu,et al.  Spatiotemporal characteristics of reference evapotranspiration during 1961-2009 and its projected changes during 2011-2099 on the Loess Plateau of China , 2012 .

[84]  Guoxiong Wu,et al.  An assessment of summer sensible heat flux on the Tibetan Plateau from eight data sets , 2012, Science China Earth Sciences.

[85]  Joan M. Galve,et al.  Temperature‐based and radiance‐based validations of the V5 MODIS land surface temperature product , 2009 .

[86]  Spatial and temporal variation patterns of reference evapotranspiration across the Qinghai‐Tibetan Plateau during 1971–2004 , 2009 .

[87]  Zhuguo Ma,et al.  Estimation of evapotranspiration over the terrestrial ecosystems in China , 2014 .

[88]  B. Li,et al.  Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area , 2009 .

[89]  W. Oechel,et al.  Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model , 2012 .

[90]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[91]  Xuelong Chen,et al.  Determination of land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau by using the MODIS and in situ data , 2011 .

[92]  José A. Sobrino,et al.  Accelerated Changes of Environmental Conditions on the Tibetan Plateau Caused by Climate Change , 2011 .

[93]  Mario Minacapilli,et al.  The impact of in-canopy wind profile formulations on heat flux estimation in an open orchard using the remote sensing-based two-source model , 2010 .

[94]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[95]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[96]  Toshio Koike,et al.  Determination of regional distributions and seasonal variations of land surface heat fluxes from Landsat‐7 Enhanced Thematic Mapper data over the central Tibetan Plateau area , 2006 .

[97]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[98]  Lei Zhang,et al.  An overview of the Semi-arid Climate and Environment Research Observatory over the Loess Plateau , 2008 .

[99]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[100]  Rogier van der Velde,et al.  Decadal variations of land surface temperature anomalies observed over the Tibetan Plateau by the Special Sensor Microwave Imager (SSM/I) from 1987 to 2008 , 2012, Climatic Change.

[101]  E. Wood,et al.  Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling , 2006 .

[102]  Toshio Koike,et al.  Improvement of surface flux parametrizations with a turbulence‐related length , 2002 .

[103]  Tandong Yao,et al.  TIBETAN OBSERVATION AND RESEARCH PLATFORM Atmosphere-Land Interaction over a Heterogeneous Landscape , 2008 .

[104]  Franz H. Berger,et al.  Regional land surface energy fluxes by satellite remote sensing in the Upper Xilin River Watershed (Inner Mongolia, China) , 2007 .

[105]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .