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Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results

Surveys in Geophysics volume 35, pages 1285–1309 (2014)Cite this article

Abstract

We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at \(5^\circ\) resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period.

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References

  • Döll P, Fritsche M, Eicker A, Müller Schmied H (2014a) Seasonal water storage variations as impacted by water abstractions: comparing the output of a global hydrological model with GRACE and GPS observations. Surv Geophys. doi:10.1093/gji/ggt485

  • Döll P, Müller Schmied H, Schuh C, Portmann FT, Eicker A (2014b) Global-scale assessment of groundwater depletion and related groundwater abstractions: combining hydrological modeling with information from well observations and GRACE satellites, Water Resour Res 50. doi:10.1002/2014WR015595  

  • Döll P, Hoffmann-Dobrev H, Portmann FT, Siebert S, Eicker A, Rodell M, Strassberg G, Scanlon B (2012) Impact of water withdrawals from groundwater and surface water on continental water storage variations. J Geodyn 59:143–156

    Article  Google Scholar 

  • Döll P, Kaspar F, Lehner B (2003) A global hydrological model for deriving water availability indicators: model tuning and validation. J Hydrol 270(1):105–134

    Article  Google Scholar 

  • Evensen G (2003) The ensemble kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367

    Article  Google Scholar 

  • Evensen G (2009) Data assimilation: the ensemble Kalman filter. Springer, Berlin

    Book  Google Scholar 

  • Forman BA, Reichle R (2013) The spatial scale of model errors and assimilated retrievals in a terrestrial water storage assimilation system. Water Resour Res 49:7457–7468. doi:10.1002/2012WR012885

    Article  Google Scholar 

  • Forman BA, Reichle RH, Rodell M (2012) Assimilation of terrestrial water storage from GRACE in a snow dominated basin. Water Resour Res 48(1). doi:10.1029/2011WR011239

  • Forootan E, Kusche J, Loth I, Schuh WD, Eicker A, Awange J, Longuevergne L, Diekkrüger B, Schmidt M, Shum C (2014) Multivariate prediction of total water storage changes over West Africa from multi-satellite data. Surv Geophys 35(4):913–940. doi:10.1007/s10712-014-9292-0

    Article  Google Scholar 

  • Geng S, Penning de Vries FW, Supit I (1986) A simple method for generating daily rainfall data. Agric For Meteorol 36(4):363–376

    Article  Google Scholar 

  • Güntner A, Stuck J, Werth S, Döll P, Verzano K, Merz B (2007) A global analysis of temporal and spatial variations in continental water storage. Water Resour Res 43(5). doi:10.1029/2006WR005247

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset. Int J Climatol 34(3):623–642. doi:10.1002/joc.3711

  • Houborg R, Rodell M, Li B, Reichle R, Zaitchik BF (2012) Drought indicators based on model-assimilated Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage observations. Water Resour Res 48(7). doi:10.1029/2011WR011291

  • Hunger M, Döll P (2008) Value of river discharge data for global-scale hydrological modeling. Hydrol Earth Syst Sci 12(3):841–861

    Article  Google Scholar 

  • Kalman RE et al (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45

    Article  Google Scholar 

  • Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Cambridge University Press, Cambridge

    Google Scholar 

  • Kaspar F (2003) Entwicklung und Unsicherheitsanalyse eines globalen hydrologischen Modells (Development and uncertainty analysis of a global hydrological model). Ph.D. thesis, University of Kassel, Germany

  • Landerer FW, Dickey JO, Güntner A (2010) Terrestrial water budget of the Eurasian pan-Arctic from GRACE satellite measurements during 2003–2009. J Geophys Res: Atmos (1984–2012) 115(D23). doi:10.1029/2010JD014584

  • Lehner B, Döll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. J Hydrol 296(1):1–22

    Article  Google Scholar 

  • Li B, Rodell M, Zaitchik BF, Reichle RH, Koster RD, van Dam TM (2012) Assimilation of GRACE terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446:103–115

  • Mayer-Gürr T, Kurtenbach E, Eicker A (2010) ITG-Grace2010 gravity field model. http://www.igg.uni-bonn.de/apmg/index.php?id=itg-grace2010

  • Moradkhani H (2008) Hydrologic remote sensing and land surface data assimilation. Sensors 8(5):2986–3004

    Article  Google Scholar 

  • Müller Schmied H, Eisner S, Franz D, Wattenbach M, Portmann FT, Flörke M, Döll P. (2014) Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration. Hydrol Earth Syst Sci 18:3511–3538. doi:10.5194/hess-18-3511-2014

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I: a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Pan M, Wood EF (2006) Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J Hydrometeorol 7(3):534–547

    Article  Google Scholar 

  • Reichle RH, McLaughlin DB, Entekhabi D (2002) Hydrologic data assimilation with the ensemble Kalman filter. Mon Weather Rev 130(1):103–114

    Article  Google Scholar 

  • Rodell M, Chen J, Kato H, Famiglietti JS, Nigro J, Wilson CR (2007) Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeol J 15(1):159–166

    Article  Google Scholar 

  • Schmidt R, Petrovic S, Güntner A, Barthelmes F, Wünsch J, Kusche J (2008) Periodic components of water storage changes from GRACE and global hydrology models. J Geophys Res: Solid Earth (1978–2012) 113(B8):doi:10.1029/2007JB005363

  • Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2014) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol, 115(1–2):15–40. doi:10.1007/s00704-013-0860-x

  • Schumacher M (2012) Assimilation of GRACE data into a hydrological model using an ensemble Kalman filter. University of Bonn, Master thesis

    Google Scholar 

  • Schumacher M, Eicker A, Kusche J, Müller Schmied H, Döll P (2014, accepted) Covariance analysis and sensitivity studies for GRACE Assimilation into WGHM, IAG 150 years, C. Rizos, P. Willis (eds), IAG Symp. 143, in press

  • Su H, Yang ZL, Dickinson RE, Wilson CR, Niu GY (2010) Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information. J Geophys Res: Atmos (1984–2012) 115(D10). doi:10.1029/2009JD013035

  • Tapley BD, Bettadpur S, Watkins M, Reigber C (2004) The gravity recovery and climate experiment: mission overview and early results. Geophys Res Lett 31(9). doi:10.1029/2004GL019920

  • Tsompanopoulos E (2010) Assimilating GRACE terrestrial water storage observations into a conceptual hydrological models. Master thesis, Delft University of Technology

  • Wahr J, Molenaar M, Bryan F (1998) Time variability of the earths gravity field: hydrological and oceanic effects and their possible detection using GRACE. J Geophys Res 103(B12):30,205–30,230

    Article  Google Scholar 

  • Walker JP, Houser PR, Reichle RH (2003) New technologies require advances in hydrologic data assimilation. Eos Trans Am Geophys Union 84(49):545–551

    Article  Google Scholar 

  • Werth S (2010) Calibration of the global hydrological model WGHM with water mass variations from GRACE gravity data. Ph.D. thesis, University of Potsdam

  • Werth S, Güntner A (2010) Calibration analysis for water storage variability of the global hydrological model WGHM. Hydrolo Earth Syst Sci 14(1):59

    Article  Google Scholar 

  • Werth S, Güntner A, Petrovic S, Schmidt R (2009) Integration of GRACE mass variations into a global hydrological model. Earth Planet Sci Lett 277(1):166–173

    Article  Google Scholar 

  • Widiastuti E (2009) Data assimilation of GRACE terrestrial water storage data into a hydrological model using the Ensemble Kalman Smoother. Master thesis, Delft University of Technology

  • Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi River basin. J Hydrometeorol 9(3):535–548

    Article  Google Scholar 

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Acknowledgments

The support of the German Research Foundation (DFG) within the framework of the Special Priority Programme “Mass transport and mass distribution in the Earth’s system” (SPP1257) is gratefully acknowledged. Furthermore, we acknowledge two anonymous reviewers and the editor, Prof. Sneeuw, whose suggestions helped to improve the manuscript.

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Authors and Affiliations

  1. Institute of Geodesy and Geoinformation, Nussallee 17, 53115, Bonn, Germany

    Annette Eicker, Maike Schumacher & Jürgen Kusche

  2. Institute of Physical Geography, Frankfurt am Main, Germany

    Petra Döll & Hannes Müller Schmied

Authors
  1. Annette Eicker
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  2. Maike Schumacher
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  3. Jürgen Kusche
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  4. Petra Döll
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  5. Hannes Müller Schmied
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Correspondence to Annette Eicker.

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Eicker, A., Schumacher, M., Kusche, J. et al. Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results . Surv Geophys 35, 1285–1309 (2014). https://doi.org/10.1007/s10712-014-9309-8

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Keywords

  • Data assimilation
  • GRACE
  • WaterGAP
  • Ensemble Kalman filter
  • Gain matrix