Sequential estimation of surface water mass changes from daily satellite gravimetry data

We propose a recursive Kalman filtering approach to map regional spatio-temporal variations of terrestrial water mass over large continental areas, such as South America. Instead of correcting hydrology model outputs by the GRACE observations using a Kalman filter estimation strategy, regional 2-by-2 degree water mass solutions are constructed by integration of daily potential differences deduced from GRACE K-band range rate (KBRR) measurements. Recovery of regional water mass anomaly averages obtained by accumulation of information of daily noise-free simulated GRACE data shows that convergence is relatively fast and yields accurate solutions. In the case of cumulating real GRACE KBRR data contaminated by observational noise, the sequential method of step-by-step integration provides estimates of water mass variation for the period 2004–2011 by considering a set of suitable a priori error uncertainty parameters to stabilize the inversion. Spatial and temporal averages of the Kalman filter solutions over river basin surfaces are consistent with the ones computed using global monthly/10-day GRACE solutions from official providers CSR, GFZ and JPL. They are also highly correlated to in situ records of river discharges (70–95 %), especially for the Obidos station where the total outflow of the Amazon River is measured. The sparse daily coverage of the GRACE satellite tracks limits the time resolution of the regional Kalman filter solutions, and thus the detection of short-term hydrological events.

[1]  L. P. Pellinen.  Physical Geodesy , 1972 .

[2]  Scott B. Luthcke,et al.  Improving global mass flux solutions from Gravity Recovery and Climate Experiment (GRACE) through forward modeling and continuous time correlation , 2010 .

[3]  S. Gratton,et al.  GRACE-derived surface water mass anomalies by energy integral approach: application to continental hydrology , 2011 .

[4]  C. Shum,et al.  Time‐variable aliasing effects of ocean tides, atmosphere, and continental water mass on monthly mean GRACE gravity field , 2004 .

[5]  C. Provost,et al.  Spectroscopy of the world ocean tides from a finite element hydrodynamic model , 1994 .

[6]  C. Jekeli.  The determination of gravitational potential differences from satellite-to-satellite tracking , 1999 .

[7]  T. Oki,et al.  Movement of Amazon surface water from time‐variable satellite gravity measurements and implications for water cycle parameters in land surface models , 2010 .

[8]  B. D. Tapley,et al.  Recent La Plata basin drought conditions observed by satellite gravimetry , 2010 .

[9]  Frédéric Frappart,et al.  Denoising Satellite Gravity Signals by Independent Component Analysis , 2010, IEEE Geoscience and Remote Sensing Letters.

[10]  Florent Lyard,et al.  Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcing ‐ comparisons with observations , 2003 .

[11]  P. Döll,et al.  Interactive comment on “ Value of river discharge data for global-scale hydrological modeling ” by M . Hunger and P . Döll , 2008 .

[12]  Bruno Merz,et al.  A global analysis of temporal and spatial variations in continental water storage , 2007 .

[13]  Torsten Mayer-Gürr,et al.  Improved daily GRACE gravity field solutions using a Kalman smoother , 2012 .

[14]  C. Shum,et al.  Non-isotropic Gaussian smoothing and leakage reduction for determining mass changes over land and ocean using GRACE data , 2010 .

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

[16]  N. G. Val’es,et al.  CNES/GRGS 10-day gravity field models (release 2) and their evaluation , 2010 .

[17]  A. Eicker,et al.  Deriving daily snapshots of the Earth's gravity field from GRACE L1B data using Kalman filtering , 2009 .

[18]  Scott B. Luthcke,et al.  FAST TRACK PAPER: Tide model errors and GRACE gravimetry: towards a more realistic assessment , 2006 .

[19]  S. Swenson,et al.  Post‐processing removal of correlated errors in GRACE data , 2006 .

[20]  A. Cazenave,et al.  Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges , 2010 .

[21]  Don P. Chambers,et al.  Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean , 2012 .

[22]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[23]  Frédéric Frappart,et al.  Changes in terrestrial water storage versus rainfall and discharges in the Amazon basin , 2013 .

[24]  Christopher Jekeli,et al.  Precise estimation of in situ geopotential differences from GRACE low‐low satellite‐to‐satellite tracking and accelerometer data , 2006 .

[25]  Srinivas Bettadpur,et al.  Impact of short period, non‐tidal, temporal mass variability on GRACE gravity estimates , 2004 .

[26]  Jean-Charles Marty,et al.  Temporal gravity field models inferred from GRACE data , 2007 .

[27]  P. Döll,et al.  A global hydrological model for deriving water availability indicators: model tuning and validation , 2003 .

[28]  Duane E. Waliser,et al.  GRACE's spatial aliasing error , 2006 .

[29]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[30]  F. Frappart,et al.  Constrained Regional Recovery of Continental Water Mass Time-variations from GRACE-based Geopotential Anomalies over South America , 2010, Surveys in Geophysics.

[31]  Grzegorz Michalak,et al.  GFZ GRACE Level-2 Processing Standards Document for Level-2 Product Release 0005 : revised edition, January 2013 , 2013 .

[32]  F. Bryan,et al.  Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE , 1998 .

[33]  Frédéric Frappart,et al.  Regional GRACE-based estimates of water mass variations over Australia: validation and interpretation , 2013 .

[34]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[35]  Willi Freeden,et al.  Spherical Functions of Mathematical Geosciences: A Scalar, Vectorial, and Tensorial Setup , 2008, Geosystems Mathematics.

[36]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[37]  Frédéric Frappart,et al.  An independent component analysis filtering approach for estimating continental hydrology in the GRACE gravity data , 2011 .

[38]  Alison Fowler.  The Ensemble Kalman filter , 2016 .

[39]  C. Prigent,et al.  Surface freshwater storage and dynamics in the Amazon basin during the 2005 exceptional drought , 2012 .

[40]  T. Oki,et al.  Dynamics of surface water storage in the Amazon inferred from measurements of inter‐satellite distance change , 2009 .

[41]  Shailen D. Desai,et al.  Observing the pole tide with satellite altimetry , 2002 .

[42]  L. V. Beek,et al.  Water balance of global aquifers revealed by groundwater footprint , 2012, Nature.

[43]  S. Calmant,et al.  1 Large-scale hydrologic and hydrodynamic modelling of the Amazon River basin 2 3 4 , 2013 .

[44]  Influence of Hydrology-Related Temporal Aliasing on the Quality of Monthly Models Derived from GRACE Satellite Gravimetric Data , 2009 .

[45]  Olga Didova,et al.  Comparisons of atmospheric mass variations derived from ECMWF reanalysis and operational fields, over 2003–2011 , 2014, Journal of Geodesy.

[46]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[47]  Grzegorz Michalak,et al.  GFZ RL05: An Improved Time-Series of Monthly GRACE Gravity Field Solutions , 2014 .