On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.

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

[2]  J. Famiglietti,et al.  A GRACE‐based water storage deficit approach for hydrological drought characterization , 2014 .

[3]  A. Sahoo,et al.  Multisource estimation of long-term terrestrial water budget for major global river basins , 2012 .

[4]  W. J. Shuttleworth,et al.  Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century , 2011 .

[5]  M. Watkins,et al.  GRACE Measurements of Mass Variability in the Earth System , 2004, Science.

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

[7]  E. Fetzer,et al.  The Observed State of the Water Cycle in the Early Twenty-First Century , 2015 .

[8]  Taikan Oki,et al.  Global atmospheric water balance and runoff from large river basins , 1995 .

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

[10]  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 .

[11]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[12]  E. Wood,et al.  Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions , 2007 .

[13]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[14]  W. Tad Pfeffer,et al.  Recent contributions of glaciers and ice caps to sea level rise , 2012, Nature.

[15]  Boleslo E. Romero,et al.  A quasi-global precipitation time series for drought monitoring , 2014 .

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

[17]  E. Wood,et al.  Estimation of the Terrestrial Water Budget over Northern Eurasia through the Use of Multiple Data Sources , 2011 .

[18]  Soroosh Sorooshian,et al.  Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network , 2007 .

[19]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

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

[21]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[22]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[23]  A. Sahoo,et al.  Reconciling the global terrestrial water budget using satellite remote sensing , 2011 .

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

[25]  Alfonso Rivera,et al.  Assessment of water budget for sixteen large drainage basins in Canada , 2014 .

[26]  C. Kummerow,et al.  Inferring the uncertainty of satellite precipitation estimates in data‐sparse regions over land , 2013 .

[27]  S. Bettadpur,et al.  Ensemble prediction and intercomparison analysis of GRACE time‐variable gravity field models , 2014 .

[28]  J. Famiglietti.  The global groundwater crisis , 2014 .

[29]  Faisal Hossain,et al.  Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data , 2015 .

[30]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[31]  U. Schneider,et al.  GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle , 2013, Theoretical and Applied Climatology.

[32]  Yudong Tian,et al.  A global map of uncertainties in satellite‐based precipitation measurements , 2010 .

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

[34]  R. Adler,et al.  Intercomparison of global precipitation products : The third Precipitation Intercomparison Project (PIP-3) , 2001 .

[35]  Qiuhong Tang,et al.  Estimating the water budget of major US river basins via remote sensing , 2010 .

[36]  Michael Durand,et al.  The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies , 2010, Proceedings of the IEEE.

[37]  Michael Durand,et al.  Assessing the potential global extent of SWOT river discharge observations , 2014 .

[38]  M. Mccabe,et al.  Closing the terrestrial water budget from satellite remote sensing , 2009 .

[39]  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 .