Improving un-gauged hydrological modeling by assimilating GRACE TWS data

Improving un-gauged hydrological modeling by

assimilating GRACE Terrestrial Water Storage

dataKangning Huang, Xia Li, Jiayong Liang, and

Xiaoping Liu

School of Geography and Planning,and Guangdong Key Laboratory for Urbanization and Geo-Simulation,

Sun Yat-sen University, Guangzhou, China

Sun Yat-sen University 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

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 3

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


SubasinsDEM

2. Methodology—Framework

Soil TypeLand UsePrecipitation

Water Recycle Simulated By SWAT TWS Observed By GRACE

𝑋 𝑡𝑓= 𝑋 𝑡

𝑓+𝐾 𝑡 [𝑌 𝑡−𝐻 ( 𝑋 𝑡𝑓 )]

Ensemble Kalman Filter：𝑋 𝑡

𝑓

Assimilation

Results


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



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)

2.3. Data assimilation• Two ways of understanding (estimating) the world:

• Modeling– Poorly known– Chaos

• Observation– Incomplete– Inaccurate


2.3 Data assimilation--Kalman Filter

Modeled State: X， Variance: DObserved State: Y， Variance: RProcess Model: M(*)Obs Operator: H(*), Y=H(X)

state

timeModeled Observed

ftX a

tX

tY

1

a fft t t t t

f T f Tt t t

X X K Y H X

K D H HD H R

• Process of Ensemble Kalman Filter

• DA of GRACE Terrestrial Water Storage Improve the Hydrological Model (SWAT)

2.3 Data assimilation—Ensemble Kalman Filter


state

timeModeled ObservedModel Error

2.3 GRACE Ensemble Kalman Smoother

SWAT Sta/Mon

GRACE Obs/Mon

SWAT Sta/Day……Rerun The Simulation Of This Month：

Surface RunoffSoil MoistureGroundwater

……

Hydrological Model

GRACE Measurements

Temporal Scale Daily simulation

Monthly anomalies

Horizontal Scale

Subasins Regionally Smoothed

Vertical Scale Various layers

All-composed

Difficulties in multi-scale

assimilation


(Zaitchik et al., 2008)

fTX

TY

a fT T T TX K Y H X

ftX

1a a at t tX M X X

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

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.

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:


3. Experimental Results


GRACE-TWS measurements in Pearl River Basin, 2005

Data Assimilation results of GRACE-SWAT, 2005

3. Experimental Results—Validation against hydrological stations


3. Experimental Results—Accuracy


MAE Std. abs err RMSEOpen Loop

(without DA) 8697.78 11652.16 14518.80

Data Assimilation of GRACE-TWS 5625.70 7657.11 9487.27

• DA skills: Improvement of accuracy due to data assimilation

• skill = 1 – RMSEOL / RMSEDA

• Total Skill: 0.35

3. Experimental Results—Skills in different subasins


Stations SkillsGaoyao 0.40

Shijiao 0.36

Lishi 0.33

Longchuan 0.34

Heyuan 0.34

Boluo 0.35

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


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.


Improving un-gauged hydrological modeling by assimilating GRACE TWS data

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