A harmonized global land evaporation dataset from model-based products covering 1980–2017

Abstract. Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25∘. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the Amazon Plain in South America and Congo Basin in central Africa and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to demonstrating a good performance, the REA method also successfully converged the models based on the reliability of the inputs. The resulting REA data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

[1]  Jian Peng,et al.  Contrasting controls on Congo Basin evaporation at the two rainfall peaks , 2020, Climate Dynamics.

[2]  N. Verhoest,et al.  Evaluating the land-surface energy partitioning in ERA5 , 2020 .

[3]  Gregory R. Quetin,et al.  Data-driven estimates of evapotranspiration and its controls in the Congo Basin , 2020, Hydrology and Earth System Sciences.

[4]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[5]  L. Tang,et al.  A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing , 2020, Journal of Forestry Research.

[6]  G. Mariéthoz,et al.  Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies , 2020, Advances in Water Resources.

[7]  J. Muller,et al.  Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework , 2020, Journal of Hydrology.

[8]  José A. Sobrino,et al.  Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[9]  L. Guanter,et al.  Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence , 2019, Agricultural and Forest Meteorology.

[10]  W. Shuttleworth.  Surface Energy Fluxes , 2019, Climate Change and Terrestrial Ecosystem Modeling.

[11]  Minha Choi,et al.  Assessment of satellite- and reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia , 2018, Agricultural and Forest Meteorology.

[12]  D. Fernández-Prieto,et al.  Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements , 2018, Hydrology and Earth System Sciences.

[13]  P. de Rosnay,et al.  ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? , 2018, Hydrology and Earth System Sciences.

[14]  Fei Ji,et al.  Temporal Characteristics of Actual Evapotranspiration Over China Under Global Warming , 2018, Journal of Geophysical Research: Atmospheres.

[15]  Minha Choi,et al.  Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach , 2018 .

[16]  Xiaoliang Lu,et al.  Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest , 2018 .

[17]  Jonas Ardö,et al.  Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method , 2017 .

[18]  S. Liang,et al.  Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. , 2017 .

[19]  C. Flynn,et al.  The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation. , 2017, Journal of climate.

[20]  William M. Putman,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[21]  Sanaa Hobeichi,et al.  Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate , 2017 .

[22]  W. Wagner,et al.  Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products , 2017 .

[23]  R. Koster,et al.  Land Surface Precipitation in MERRA-2 , 2017 .

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

[25]  Qiang Zhang,et al.  Evaluation of evapotranspiration models over semi‐arid and semi‐humid areas of China , 2016 .

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

[27]  Xuelong Chen,et al.  Comparison of satellite based evapotranspiration estimates over the Tibetan Plateau , 2016 .

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

[29]  Chunlin Huang,et al.  Multi‐model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging , 2016 .

[30]  Park JongMin,et al.  Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite‐based and ground‐based data , 2016 .

[31]  Shunlin Liang,et al.  An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations , 2016, PloS one.

[32]  Lei Wang,et al.  A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method , 2016 .

[33]  Jian Yu,et al.  Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations , 2016 .

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

[35]  Minha Choi,et al.  Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images , 2015 .

[36]  Seokhyeon Kim,et al.  A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation , 2015 .

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

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

[39]  Chaoyang Wu,et al.  A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics , 2015 .

[40]  Minha Choi,et al.  Spatio‐temporal distribution of actual evapotranspiration in the Indus Basin Irrigation System , 2015 .

[41]  F. Aires,et al.  Combining data sets of satellite‐retrieved products for basin‐scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model , 2014 .

[42]  L. Guanter,et al.  The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange , 2014 .

[43]  F. Aires.  Combining Datasets of Satellite-Retrieved Products. Part I: Methodology and Water Budget Closure , 2014 .

[44]  Compton J. Tucker,et al.  A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series , 2014, Remote. Sens..

[45]  S. Liang,et al.  Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations , 2014 .

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

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

[48]  E. Harmsen,et al.  Evapotranspiration : Principles and Applications for Water Management , 2013 .

[49]  Wei Gao,et al.  Improved sensible and latent heat flux estimation of community land model by using ensemble Kalman filter assimilation , 2013, Optics & Photonics - Optical Engineering + Applications.

[50]  Wim G.M. Bastiaanssen,et al.  First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure , 2013 .

[51]  S. Seneviratne.  Climate science: Historical drought trends revisited , 2012, Nature.

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

[53]  Wade T. Crow,et al.  An objective methodology for merging satellite‐ and model‐based soil moisture products , 2012 .

[54]  S. Seneviratne,et al.  Hot days induced by precipitation deficits at the global scale , 2012, Proceedings of the National Academy of Sciences.

[55]  W. Bastiaanssen,et al.  Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin , 2012 .

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

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

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

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

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

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

[62]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[63]  F. Giorgi,et al.  Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections , 2010 .

[64]  Jeffrey P. Walker,et al.  Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study , 2008 .

[65]  D. Donoghue,et al.  Intercomparison of Evapotranspiration Over the Savannah Volta Basin in West Africa Using Remote Sensing Data , 2008, Sensors.

[66]  E. Wood,et al.  Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off‐line simulation of the terrestrial hydrologic cycle , 2007 .

[67]  T. Foken,et al.  Some aspects of the energy balance closure problem , 2006 .

[68]  S. Seneviratne,et al.  Land–atmosphere coupling and climate change in Europe , 2006, Nature.

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

[70]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

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

[72]  F. Giorgi,et al.  Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method , 2002 .

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

[74]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[75]  T. Jiang,et al.  A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980–2017 , 2021 .

[76]  Sonia I. Seneviratne,et al.  Observational evidence for soil-moisture impact on hot extremes in southeastern Europe , 2011 .

[77]  Christoph Schillings,et al.  Satellite-Based Actual Evapotranspiration over Drying Semiarid Terrain in West Africa , 2007 .

[78]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.