Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.

[1]  Marvin E. Jensen,et al.  Lysimeters for Evapotranspiration and Environmental Measurements , 1991 .

[2]  Markus Disse,et al.  Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. , 2016, The Science of the total environment.

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

[4]  N. Verhoest,et al.  GLEAM v3: satellite-based land evaporation and root-zone soil moisture , 2016 .

[5]  W. Dorigo,et al.  A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset , 2016 .

[6]  S. Running,et al.  A review of remote sensing based actual evapotranspiration estimation , 2016 .

[7]  Xiaotong Zhang,et al.  Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis , 2018 .

[8]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .

[9]  F. Pappenberger,et al.  Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling , 2017 .

[10]  S. Goetz,et al.  Satellite remote sensing of surface energy balance : success, failures, and unresolved issues in FIFE , 1992 .

[11]  Jaap Schellekens,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data , 2016 .

[12]  Kellie B. Vaché,et al.  Evaluation of evapotranspiration methods for model validation in a semi-arid watershed in northern China , 2007 .

[13]  J. Monteith,et al.  Principles of Environmental Physics , 2014 .

[14]  I. S. Bowen The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface , 1926 .

[15]  R. Kormann,et al.  An Analytical Footprint Model For Non-Neutral Stratification , 2001 .

[16]  Gholam Reza Rakhshandehroo,et al.  Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran , 2017 .

[17]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[18]  Martha C. Anderson,et al.  The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources , 2017 .

[19]  A. Goldstein,et al.  What the towers don't see at night: nocturnal sap flow in trees and shrubs at two AmeriFlux sites in California. , 2007, Tree physiology.

[20]  E. Wood,et al.  Little change in global drought over the past 60 years , 2012, Nature.

[21]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms , 2015 .

[22]  Matthew F. McCabe,et al.  Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors , 2006 .

[23]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[24]  Hans Peter Schmid,et al.  Footprint modeling for vegetation atmosphere exchange studies: a review and perspective , 2002 .

[25]  Li Zhang,et al.  Multiscale Validation of the 8-day MOD16 Evapotranspiration Product Using Flux Data Collected in China , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[27]  Xuezhi Feng,et al.  Accuracy assessment of seven global land cover datasets over China , 2017 .

[28]  Jefferson S. Wong,et al.  Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada , 2017 .

[29]  Xiuping Li,et al.  Evaluation of evapotranspiration estimates for two river basins on the Tibetan Plateau by a water balance method , 2013 .

[30]  Dennis D. Baldocchi,et al.  Surface energy-balance closure over rangeland grass using the eddy covariance method and surface renewal analysis , 2008 .

[31]  T. I. Eldho,et al.  Assessment of uncertainties in global land cover products for hydro‐climate modeling in India , 2017 .

[32]  Ming Chang,et al.  Long-term Atmospheric Visibility, Sunshine Duration and Precipitation Trends in South China , 2015 .

[33]  Wenbin Liu,et al.  Temporal variation of wind speed in China for 1961–2007 , 2011 .

[34]  A. Sörensson,et al.  Intercomparison and Uncertainty Assessment of Nine Evapotranspiration Estimates Over South America , 2018 .

[35]  S. Kobayashi,et al.  The JRA-25 Reanalysis , 2007 .

[36]  H. Komatsu,et al.  Forest categorization according to dry‐canopy evaporation rates in the growing season: comparison of the Priestley–Taylor coefficient values from various observation sites , 2005 .

[37]  Randal D. Koster,et al.  Assessment of MERRA-2 Land Surface Energy Flux Estimates , 2018 .

[38]  Keith Haines,et al.  Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  Michael G. Bosilovich,et al.  Atmospheric Water Balance and Variability in the MERRA-2 Reanalysis , 2017 .

[40]  Claudia Notarnicola,et al.  Two-source energy balance modeling of evapotranspiration in Alpine grasslands , 2018 .

[41]  Kun Yang,et al.  Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties , 2018, Remote. Sens..

[42]  Eva Rubio,et al.  Analysis of the energy balance closure over a FLUXNET boreal forest in Finland , 2010 .

[43]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets , 2015 .

[44]  Guohe Huang,et al.  Modelling and measurement of two-layer-canopy interception losses in a subtropical evergreen forest of central-south China , 2005 .

[45]  Jiankai Wang,et al.  Decadal variability of surface incident solar radiation over China: Observations, satellite retrievals, and reanalyses , 2015 .

[46]  L. Wald,et al.  Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface , 2015 .

[47]  Adam J. Purdy,et al.  Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally , 2016 .

[48]  M. R. Kousari,et al.  An investigation on reference crop evapotranspiration trend from 1975 to 2005 in Iran , 2012 .

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

[50]  J. Nippert,et al.  Drivers of nocturnal water flux in a tallgrass prairie , 2018 .

[51]  Kathleen Neumann,et al.  Challenges in using land use and land cover data for global change studies , 2011 .

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

[53]  Diego G. Miralles,et al.  Magnitude and variability of land evaporation and its components at the global scale , 2011 .

[54]  Renaud Mathieu,et al.  Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa , 2014, Remote. Sens..

[55]  D. Long,et al.  Comparison of three dual‐source remote sensing evapotranspiration models during the MUSOEXE‐12 campaign: Revisit of model physics , 2015 .

[56]  B. Scanlon,et al.  Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites , 2014 .

[57]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[58]  Matthew F. McCabe,et al.  The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data , 2015 .

[59]  Jacques Roy,et al.  Processes driving nocturnal transpiration and implications for estimating land evapotranspiration , 2015, Scientific Reports.

[60]  Martin Wild,et al.  Combined surface solar brightening and increasing greenhouse effect support recent intensification of the global land‐based hydrological cycle , 2008 .

[61]  Yi Y. Liu,et al.  Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth , 2011 .

[62]  Kaicun Wang,et al.  Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets , 2018, Remote. Sens..

[63]  R. Müller,et al.  Evaluation of Radiation Components in a Global Freshwater Model with Station-Based Observations , 2016 .

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

[65]  Keith Beven,et al.  A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates , 1979 .

[66]  R. Fensholt,et al.  Evaluation of earth observation based long term vegetation trends - Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data , 2009 .

[67]  W. Ju,et al.  On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China , 2017, Climate Dynamics.

[68]  S. J. Birks,et al.  Terrestrial water fluxes dominated by transpiration , 2013, Nature.

[69]  N. Verhoest,et al.  El Niño-La Niña cycle and recent trends in continental evaporation , 2014 .

[70]  Shanlei Sun,et al.  Spatial and Temporal Variability of Precipitation and Dryness/Wetness During 1961–2008 in Sichuan Province, West China , 2014, Water Resources Management.

[71]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[72]  Xiufeng He,et al.  Multi‐model and multi‐sensor estimations of evapotranspiration over the Volta Basin, West Africa , 2015 .

[73]  H. Schmid,et al.  A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP) , 2015 .

[74]  Akhilesh S. Nair,et al.  Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India , 2017 .

[75]  Renaud Mathieu,et al.  An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa , 2017, Remote. Sens..

[76]  G. Balsamo,et al.  The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data , 2014 .

[77]  Michael G. Bosilovich,et al.  Global Energy and Water Budgets in MERRA , 2011 .

[78]  Eric A. Smith,et al.  Surface flux measurements in FIFE: An overview , 1992 .

[79]  P. L. Finkelstein,et al.  Sampling error in eddy correlation flux measurements , 2001 .

[80]  Carlos Jimenez,et al.  On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets , 2015 .

[81]  Sven Kralisch,et al.  Assessment of the influence of land use data on the water balance components of a peri-urban catchment using a distributed modelling approach , 2013 .

[82]  K. Snyder,et al.  Night-time conductance in C3 and C4 species: do plants lose water at night? , 2003, Journal of experimental botany.

[83]  Shunlin Liang,et al.  Continuous tree distribution in China: A comparison of two estimates from Moderate‐Resolution Imaging Spectroradiometer and Landsat data , 2006 .

[84]  Lijuan Cao,et al.  Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records , 2017 .

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

[86]  Corinna Rebmann,et al.  Available energy and energy balance closure at four coniferous forest sites across Europe , 2009 .

[87]  Richard G. Allen,et al.  Evapotranspiration information reporting: II. Recommended documentation , 2011 .

[88]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[89]  Vijay P. Singh,et al.  Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions , 2005 .

[90]  Seung Oh Lee,et al.  Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia , 2012 .

[91]  Tim R. McVicar,et al.  Global evaluation of four AVHRR-NDVI data sets: Intercomparison and assessment against Landsat imagery , 2011 .

[92]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

[93]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[94]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[95]  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 .

[96]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[97]  Yue Ying,et al.  Trends in Precipitable Water and Relative Humidity in China: 1979–2005 , 2011 .

[98]  Feng Zhang,et al.  Evapotranspiration and crop coefficient for a temperate desert steppe ecosystem using eddy covariance in Inner Mongolia, China , 2012 .

[99]  Pamela L. Nagler,et al.  Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration , 2007 .

[100]  Yanzhao Zhou,et al.  Assessing the impacts of an ecological water diversion project on water consumption through high-resolution estimations of actual evapotranspiration in the downstream regions of the Heihe River Basin, China , 2018 .

[101]  Shanlei Sun,et al.  Changing characteristics of precipitation during 1960–2012 in Inner Mongolia, northern China , 2015, Meteorology and Atmospheric Physics.

[102]  Shanlei Sun,et al.  Detection of trends in precipitation during 1960–2008 in Jiangxi province, southeast China , 2013, Theoretical and Applied Climatology.

[103]  T. Foken The energy balance closure problem: an overview. , 2008, Ecological applications : a publication of the Ecological Society of America.

[104]  Gabriel G. Katul,et al.  Nocturnal evapotranspiration in eddy-covariance records from three co-located ecosystems in the Southeastern U.S.: Implications for annual fluxes , 2009 .

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

[106]  Luis S. Pereira,et al.  Modelling surface resistance from climatic variables , 2000 .

[107]  Yanlian Zhou,et al.  Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes , 2018 .

[108]  Yan Shen,et al.  Validation and comparison of a new gauge‐based precipitation analysis over mainland China , 2016 .

[109]  S. Sorooshian,et al.  A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons , 2018 .

[110]  Di Long,et al.  Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements , 2017 .

[111]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[112]  Tong Jiang,et al.  Estimation of Actual Evapotranspiration by Complementary Theory-Based Advection-Aridity Model in the Tarim River Basin, China , 2017 .

[113]  Jianping Huang,et al.  Global semi-arid climate change over last 60 years , 2016, Climate Dynamics.

[114]  Peter E. Thornton,et al.  The Partitioning of Evapotranspiration into Transpiration, Soil Evaporation, and Canopy Evaporation in a GCM: Impacts on Land–Atmosphere Interaction , 2007 .

[115]  Weimin Ju,et al.  On the attribution of the changing hydrological cycle in Poyang Lake Basin, China , 2014 .

[116]  Joshua B. Fisher,et al.  What controls the error structure in evapotranspiration models , 2013 .

[117]  F. Gellens-Meulenberghs,et al.  Assessing the impact of land cover map resolution and geolocation accuracy on evapotranspiration simulations by a land surface model , 2014 .

[118]  Mingguo Ma,et al.  Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China , 2013 .

[119]  J. Deardorff Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[120]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[121]  Kul Khand,et al.  Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests , 2017, Remote. Sens..

[122]  Xi Chen,et al.  Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale , 2016 .

[123]  F. Baret,et al.  GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products , 2013 .

[124]  Shunlin Liang,et al.  Evaluation of four long time-series global leaf area index products , 2017 .

[125]  B. Henderson-Sellers,et al.  A new formula for latent heat of vaporization of water as a function of temperature , 1984 .

[126]  Matthew F. McCabe,et al.  Effects of spatial aggregation on the multi-scale estimation of evapotranspiration , 2013 .

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

[128]  L. S. Pereira,et al.  Evapotranspiration information reporting: I. Factors governing measurement accuracy , 2011 .

[129]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[130]  Gabriel B. Senay,et al.  Modeling Landscape Evapotranspiration by Integrating Land Surface Phenology and a Water Balance Algorithm , 2008, Algorithms.

[131]  Randal D. Koster,et al.  Assessment of MERRA-2 Land Surface Hydrology Estimates , 2017 .

[132]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[133]  Ziwei Xu,et al.  Assessment of the Energy Balance Closure under Advective Conditions and Its Impact Using Remote Sensing Data , 2017 .

[134]  Dario Papale,et al.  Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis , 2015 .

[135]  Xuhui Lee,et al.  Correcting surface solar radiation of two data assimilation systems against FLUXNET observations in North America , 2013 .

[136]  A. Martínez-cob,et al.  Estimating sensible and latent heat fluxes over rice using surface renewal , 2006 .

[137]  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 .

[138]  Armel Thibaut Kaptué Tchuenté,et al.  Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[139]  Eric F. Wood,et al.  Comparison and evaluation of gridded radiation products across northern Eurasia , 2009 .

[140]  J. Stewart Modelling surface conductance of pine forest , 1988 .

[141]  T. Oki,et al.  Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results , 2011 .

[142]  K. Taylor Summarizing multiple aspects of model performance in a single diagram , 2001 .

[143]  C. S. Everson,et al.  Feasibility study on the determination of riparian evaporation in non-perennial systems. , 2009 .

[144]  Markus Reichstein,et al.  Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis , 2013 .

[145]  Daniel Taylor,et al.  Rates of nocturnal transpiration in two evergreen temperate woodland species with differing water-use strategies. , 2010, Tree physiology.

[146]  Hans Bergström,et al.  Organized Turbulence Structures in the Near-Neutral Atmospheric Surface Layer , 1996 .

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