Improving un-gauged hydrological modeling by assimilating GRACE Terrestrial Water Storage data

1. Improving un-gauged hydrological modeling by assimilating GRACE Terrestrial Water Storage data Kangning Huang, Xia Li, Jiayong Liang, and Xiaoping Liu School of Geography and Planning, and Guangdong Key Laboratoryfor Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou, China

2. Content • 1. Introduction • 2. Methodology • 2.1 GRACE Terrestrial Water Storage • 2.2 The Soil and Water Assessment Tool • 2.3 Data assimilation—Ensemble Kalman Smoother • 3. Experiments • 3.1 Study site—the Pearl River Basin • 3.2 Results • 4. Conclusion Sun Yat-sen University

3. 1. Introduction • Hydrologic modeling is crucial • for water resource managements & flood predictions • It requires meteorological data: • e.g. temperature, precipitation, humidity, wind & solar • Challenge for sparsely gauged or un-gauged regions: • e.g. less developed countries, semi-arid regions, etc. Sun Yat-sen University

4. 1. Introduction • Alternative to meteorological stations • Meteorological reanalysis data • data derived from reanalysis of meteorological models • But, reanalysis data driven modeling can be less reliable • Improve un-gauged hydrological modeling, by assimilating GRACE Terrestrial Water Storage (TWS) data Sun Yat-sen University

5. 2. Methodology—Framework Ensemble Kalman Filter： DEM Subasins Soil Type Land Use Precipitation Assimilation Results Water Recycle Simulated By SWAT TWS Observed By GRACE Sun Yat-sen University

6. 2.1 GRACE TWS • Gravity Recovery And Climate Experiment (GRACE) twin satellites system • Measure gravity by relating it to the distance between the 2 satellites • Launched in 2002 • GRACE CSR RL 05by Univ of Colorado Sun Yat-sen University

7. 2.2 Soil and Water Assessment Tool • Physical-based semi-distributed hydrological model. • Subasins are linked into a tree-structure. (Xie X & Zhang D, 2010) Sun Yat-sen University

8. 2.3. Data assimilation • Two ways of understanding (estimating) the world: • Modeling • Poorly known • Chaos • Observation • Incomplete • Inaccurate Sun Yat-sen University

9. 2.3 Data assimilation--Kalman Filter state Modeled State: X，Variance: D Observed State: Y，Variance: R Process Model: M(*) Obs Operator: H(*), Y=H(X) time Modeled Observed

10. 2.3 Data assimilation—Ensemble Kalman Filter • Process of Ensemble Kalman Filter • DA of GRACE Terrestrial Water Storage  Improve the Hydrological Model (SWAT) state time Modeled Model Error Observed Sun Yat-sen University

11. 2.3 GRACE Ensemble Kalman Smoother Difficulties in multi-scale assimilation Surface Runoff Soil Moisture Groundwater GRACEObs/Mon SWATSta/Day …… …… SWATSta/Mon Rerun The Simulation Of This Month： (Zaitchiket al., 2008) Sun Yat-sen University

12. 3. Experiments in Pearl River Basin • Bears the Pearl River Delta (PRD) • PRD contributes 20% of national GDP and 40% of export. • Available observations to validate model Sun Yat-sen University

13. 3. Experiments—meteorological forcing data • Reanalysis data from CFSR： • Temperature • Precipitation • Humidity • Wind speed • Solar radiation • CFSR:Climate Forecast System Reanalysis • Provided by the National Center of Atmospheric Research, U.S. Sun Yat-sen University

14. 3. Experiments—configuration • Warm-up period: 2000~2002 • Simulation period: 2003~2005 • Initial state perturbation: * N(1, 0.2) • Ensemble size: 20 • Match of GRACE and SWAT TWS: Sun Yat-sen University

15. 3. Experimental Results GRACE-TWS measurements in Pearl River Basin, 2005 Data Assimilation results of GRACE-SWAT, 2005 Sun Yat-sen University

16. 3. Experimental Results—Validation against hydrological stations Sun Yat-sen University

17. 3. Experimental Results—Accuracy • DA skills: Improvement of accuracy due to data assimilation • skill = 1 – RMSEOL / RMSEDA • Total Skill: 0.35 Sun Yat-sen University

18. 3. Experimental Results—Skills in different subasins Sun Yat-sen University

19. 4. Conclusion & Future studies • Conclusions: • Data assimilation of GRACE can improve the streamflow simulation of hydrological model. • Potentially useful in sparsely gauged or un-gauged regions • Issues to be addressed: • Violation of the water balance equation • Scale of Assimilation • More validations Sun Yat-sen University

20. 5. Important References • Evensen G. The ensemble Kalman filter: Theoretical formulation and practical implementation[J]. Ocean dynamics, 2003, 53(4): 343-367. • Z. Q. Liu and F. Rabier. 2002. The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study. Q. J. R. Meteorol. Soc. (2002), 128, pp. 1367–1386. • Bondarenko V, Ochotta T, Saupe D, et al. The interaction between model resolution, observation resolution and observation density in data assimilation: a two-dimensional study[C]//Preprints, 11th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, San Antonio, TX, Amer. Meteor. Soc. P. 2007, 5. • Wahr J, Swenson S, Velicogna I. Accuracy of GRACE mass estimates[J]. Geophysical Research Letters, 2006, 33(6): L06401. • Xie X, Zhang D. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter[J]. Advances in Water Resources, 2010, 33(6): 678-690. • Reichle R, Zaitchik B, Rodell M, et al. Assimilation of GRACE terrestrial water storage data into a land surface model[C]//23rd Conference on Hydrology. 2009. Sun Yat-sen University