Integration Frameworks for Merging Satellite Remote Sensing Observations with Hydrological Model Outputs (original) (raw)

Abstract

Khaki et al. (2018a) Remote Sensing of Environment 3 (i) Khaki et al. (2017a) Advances in Water Resources 4 (ii) Khaki et al. (2017b) Advances in Water Resources 5 (iii) Khaki et al. (2018b) Science of The Total Environment 6 (iv) Khaki et al. (2018c) Advances in Water Resources 6 (iv) Khaki et al. (2018d) Science of The Total Environment 6 (iv) Khaki et al. (2017c) Journal of Hydrology 7 (v) Khaki et al. (2018e) Journal of Hydrology 7 (v) 2.2 Assimilation observations Various datasets are used for data assimilation in order to improve model simulations. The main used data is GRACE TWS between 2002 and 2013. In order to derive this product, the GRACE spherical harmonic coefficients up to degree and order 90 with their full error information are acquired from the ITSG-Grace2014 gravity field model (Mayer-Gürr et al., 2014). Traditionally, low degree coefficients are replaced with more accurate estimates, which includes degree 1 coefficients from Swenson et al. (2008) to account for the movement of the Earth's centre of mass and degree 2 and order 0 (C20) coefficients from Satellite Laser Ranging solutions (e.g., Chen et al., 2007). Furthermore, post glacial rebound is considered based on the estimated corrections by Geruo et al. (2013). Various filtering methods are also applied and tested to account for the colored/correlated noise in GRACE products (see details in Chapter 3). Note that in some applications presented in this thesis, filters other than the newly proposed KeFIn are used because of the fact that this filter has not been finalised or evaluated at the time of the respective studies. The L2 gravity fields are then converted to TWS fields at various spatial resolutions, e.g., 1 • , 2 • , 3 • , 4 • , 5 • , and also a basin averaged scale following Wahr et al. (1998). Satellite soil moisture products are also applied for data assimilation either along or separate from GRACE TWS. The soil moisture measurements are derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) for the period 2002-2011 and from European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) Earth Explorer mission for the period 2011-2013. SMOS and AMSR-E are selected from ascending and descending passes subject to their higher agreement to in-situ measurements (see, e.g.,

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  922. +,-ÿ /01) ,ÿ 1*02 +ÿ ( ) 2 -+3) ,4ÿ 5( 6789'ÿ 10-+2 2 ) -+ÿ :35;<*-1
  923. =>#?@A& BC ÿ DE0F) G 'C ÿ H5355-0,G BC ÿ D<E,G IC 8J0,4+G KC ÿ K5,4<+L+34,+G MC N0;0 O>P$ " QR#" @S&7+T5-+ÿ U+,1) ,4ÿ 5( ÿ ',L) 35,T+,- O>P$ " V?%A& '2 1+L) +3
  924. WR#%& I0,<03Xÿ YZ[\ ]ÿ YZ[^ÿ '2 1+L) +3ÿ _,*C ÿ 82 2 ÿ 3) 4E-1ÿ 3+1+3L+
  925. 4E-1K) ,Fÿ <1) ,4ÿ X5<3 *5:X3) 4E-C *5Tÿ *3+;+,-) 02 1C 82 3+0;Xÿ Rÿ j" f?#Vk" Slÿ >V%Aÿ 53 J0,-ÿ -5ÿ 2 +03,ÿ T53+m
  926. n2 +01+ÿ ,5-+ÿ -E0-G ÿ 01ÿ -E+ÿ 0<-E53ÿ 5( ÿ -E) 1ÿ '2 1+L) +3ÿ 03-) *2 +G ÿ X5<ÿ 3+-0) ,ÿ -E+ÿ 3) 4E-ÿ -5ÿ ) ,*2 <;+ÿ ) -ÿ ) ,ÿ 0ÿ -E+1) 1ÿ 53
  927. -ÿ ) 1ÿ ,5-ÿ :</2 ) 1E+;ÿ *5TT+3*) 02 2 XC ÿ ÿ n+3T) 11) 5,ÿ ) 1ÿ ,5-ÿ 3+o<) 3+;G ÿ /<-ÿ :2 +01+ÿ +,1<3+ -E0-ÿ X5<ÿ 3+( +3+,*+ÿ -E+ÿ p 5<3,02 ÿ 01ÿ -E+ÿ 53) 4) ,02 ÿ 15<3*+C ÿ ÿ H53ÿ T53+ÿ ) ,( 53T0-) 5,ÿ 5,ÿ -E) 1ÿ 0,;ÿ 5,ÿ X5<3ÿ 5-E+3 3+-0) ,+;ÿ 3) 4E-1G ÿ :2 +01+ÿ L) 1) -qÿ E--:1qrrJJJC +2 1+L) +3C *5Tr0/5<-r5<3s /<1) ,+11r:52 ) *) +1r*5:X3) 4E-t8<-E53s3) 4E-1 95:X3) 4E-ÿ ]ÿ YZ[\ÿ 95:X3) 4E-ÿ 92 +030,*+ÿ 9+,-+3G ÿ _,*C ÿ 82 2 ÿ 7) 4E-1ÿ 7+1+3L+;C ÿ n3) L0*Xÿ 1-0-+T+,-C ÿ v+3T1ÿ 0,;ÿ 95,;) -) 5,1C ÿ 95TT+,-1mÿ N+ÿ J5<2 ;
  928. F+ÿ -5ÿ E+03ÿ ( 35Tÿ X5<C ÿ 'sT0) 2 ÿ <1ÿ 0-ÿ *<1-5T+3*03+w*5:X3) 4E-C *5Tÿ !" #$ %& '(()*+, -+.ÿ 0 )1ÿ 234, -45 ()1165 4, -)+ÿ 611)12ÿ -+ÿ , 76 422-8-5 4, -)
  929. SAT$ " UV#" CW&'>P4+(62ÿ -+ÿ X4, 61ÿ :62)*1(62 SAT$ " YB%D& <5 26P-61
  930. ZV#%& [(, )\61ÿ ]^_à
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  932. F ()8ÿ (16>6+, -45 2F '5 164>=ÿ Vÿ l" hB#Ym" Wnÿ AY%Dÿ )1 N4+, ÿ , )ÿ 5 641+ÿ 8)16o
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  934. >-2261, 4, -)+I ÿ 31)P->6>ÿ -, ÿ -2ÿ +), ÿ 3*\5 -276>ÿ ()8861(-45 5 =F ÿ ÿ p618-22-)+ÿ -2ÿ +), ÿ 16q*-16>I ÿ \*, ÿ 35 6426ÿ 6+2*16 , 74, ÿ =)*ÿ 160 616+(6ÿ , 76ÿ R )*1+45 ÿ 42ÿ , 76ÿ )1-.-+45 ÿ 2)*1(6F ÿ ÿ K)1ÿ 8)16ÿ -+0 )184, -)+ÿ )+ÿ , 7-2ÿ 4+>ÿ )+ÿ =)*1ÿ ), 761 16, 4-+6>ÿ 1-.7, 2I ÿ 35 6426ÿ P-2-, rÿ 7, , 32rssNNNF 65 26P-61F
  935. *, s)*1t
  936. \*2-+622s3)5 -(-62s()3=1-.7, u'*, 7)1t1-.
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  938. 886+, 2oÿ X6ÿ N)*5 >ÿ 5 -H6ÿ , )ÿ 7641ÿ 0 1)8ÿ =)*F ÿ <t84-5 ÿ *2ÿ 4, ÿ (*2, )861(416y()3=1-.7, F ()8ÿ
  939. @A#BCD& EF ÿ G61H= I JF ÿ K.:..* 12I EF ÿ G+62I LF '<1235I KF ÿ M1>1I NF GF ÿ O6+?
  940. >-:= 36* F ;.?ÿ ;:5,52* = 14 )F '4 :51,-ÿ Sÿ k" eB#Wl" Tmÿ AW%Dÿ .: <12* ÿ * .ÿ 4 51:2ÿ ?.:5n
  941. * 5ÿ * 61* I ÿ 1)ÿ * 65ÿ 1+* 6.:ÿ ./ ÿ * 6= )ÿ J4 )5V= 5:ÿ 1:* = ;4 5I ÿ -.+ÿ :5* 1= 2ÿ * 65ÿ := 36* ÿ * .ÿ = 2;
  942. +,5ÿ = * ÿ = 2ÿ 1ÿ * 65)= )ÿ .: ,= ))5
  943. *
  944. * = .2I ÿ >:.V= ,5,ÿ = * ÿ = )ÿ 2.* ÿ >+84 = )65,ÿ ;.??5:;= 14 4 -F ÿ ÿ M5:?= ))= .2ÿ = )ÿ 2.* ÿ :5o+= :5,I ÿ 8+* ÿ >4 51)5ÿ 52)+:5 * 61* ÿ -.+ÿ :5/ 5:52;5ÿ * 65ÿ p .+:214 ÿ 1)ÿ * 65ÿ .:= 3= 214 ÿ ).+:;5F ÿ ÿ K.:ÿ ?.:5ÿ = 2/ .:?1* = .2ÿ .2ÿ * 6= )ÿ 12,ÿ .2ÿ -.+:ÿ .* 65: :5* 1= 25,ÿ := 36* )I ÿ >4 51)5ÿ V= )= * qÿ 6* * >)qrr<<<F 54 )5V= 5:F ;.?r18.+* r.+:9
  945. = 25))r>.4 = ;= 5)r;.>-:= 36* s'+* 6.:9:= 36* )
  946. N.>-:= 36* ÿ ]ÿ Z[Y\ÿ N.>-:= 36* ÿ N4 51:12;5ÿ N52* 5:I ÿ u2;F ÿ '4 4 ÿ i= 36* )ÿ i5)5:V5,F ÿ M:= V1;-ÿ )* 1* 5?52* F ÿ U5:?)ÿ 12,ÿ N.2,= * = .2)F ÿ N.??52* )nÿ v5ÿ <.+4 ,ÿ 4 = H5ÿ * .ÿ 651:ÿ / :.?ÿ -.+F ÿ J9?1= 4 ÿ +)ÿ 1* ÿ ;
  947. 9 6 ) 1:ÿ 8 +:ÿ 6 02/ÿ ;</:+6 +=9 >06 ÿ /0) 0
  948. >+23) :09 2) ?@#ABC& DE ÿ 7;0F9 G HE ÿ '9 ) ,I6 ,JK-9 ;
  949. G LE M+) 19 ) G IE ÿ J+:++) 02G NE ÿ '*02=1G DE 7-;
  950. O@P$ " QR#" BS&N+-:206 ÿ +8 ÿ M</:+6 +=<
  951. O@P$ " TA%C& I6 31U9 1: VR#%& W1>1451:ÿ XYZ[ \ÿ XYZ[ÿ I6 31U9 1:ÿ HE ]E ÿ '6 6 ÿ :9 =;) 3ÿ :131:U1/E 10:2ÿ 4+:1m
  952. +:ÿ +8 ÿ ) ;
  953. 9 3ÿ I6 31U9 1:ÿ 0:) 9 >6 1G ÿ <+-ÿ :1) 09 2ÿ ) ;1ÿ :9 =;) ÿ ) +ÿ 9 2>6 -/1ÿ
  954. 9 +2G ÿ .:+U9 /1/ÿ 9 ) ÿ 9 3ÿ 2+) ÿ .-56 9 3;1/ÿ >+441:>9 06 6 <E ÿ ÿ n1:49 339 +2ÿ 9 3ÿ 2+) ÿ :1K-9 :1/G ÿ 5-) ÿ .6 1031ÿ 123-:1 ) ;
  955. 9 3ÿ 02/ÿ +2ÿ <+-:ÿ +) ;1: :1) 09 21/ÿ :9 =;) 3G ÿ .6 1031ÿ U9 39 ) pÿ ;
  956. ***E 16 31U9 1:E >+4q05+-) q+-:, 5-39 2133q.+6 9 >9 13q>+.<:9 =;) r'-) ;
  957. ÿ ÿ ÿ ÿ ÿ t+.<:9 =;) ÿ \ÿ XYZuÿ t+.<:9 =;) ÿ t6 10:02>1ÿ t12) 1:G ÿ L2>E ÿ '6 6 ÿ h9 =;) 3ÿ h131:U1/E ÿ n:9 U0><ÿ 3) 0) 1412) E ÿ v1:43ÿ 02/ÿ t+2/9 ) 9 +23E ÿ t+4412) 3mÿ w1ÿ *+-6 /ÿ
  958. Khaki, M., Forootan, E., Kuhn, M., Awange, J., Longuevergne, L., Wada, Y., (2018a). Effi- cient Basin Scale Filtering of GRACE Satellite Products. Remote Sensing of Environment, 204:76-93, doi:10.1016/j.rse.2017.10.040.
  959. Khaki, M., Schumacher, M., J., Forootan, Kuhn, M., Awange, E., van Dijk, A.I.J.M., (2017a). Accounting for Spatial Correlation Errors in the Assimilation of GRACE into Hydrological Models through localization. Advances in Water Resources, 108:99-112, doi:10.1016/j.advwatres.2017.07.024.
  960. Khaki, M., Hoteit, I., Kuhn, M., Awange, J., Forootan, E., van Dijk, A.I.J.M., Schumacher, M., Pattiaratchi, C., (2017b). Assessing sequential data assimilation techniques for integrat- ing GRACE data into a hydrological model. Advances in Water Resources, 107:301-316, doi:10.1016/j.advwatres.2017.07.001.
  961. Khaki, M., Forootan, E., Kuhn, M., Awange, J., Papa, F., Shum, C.K., (2018b). A Study of Bangladesh's Sub-surface Water Storages Using Satellite Products and Data Assimilation Scheme. Science of The Total Environment, 625:963-977, doi:10.1016/j.scitotenv.2017.12.289.
  962. Khaki, M., Forootan, E., Kuhn, M., Awange, J., van Dijk, A.I.J.M., Schumacher, M., Sharifi, M.A., (2018c). Determining Water Storage Depletion within Iran by Assimilating GRACE data into the W3RA Hydrological Model. Advances in Water Resources, 114:1-18, doi:10.1016/j.advwatres.2018.02.008.
  963. Khaki, M., Awange, J. (2018d). The Application of Remotely Sensed Products to Enhance Model-derived Water Storage Changes over South America. Revised and resubmitted to Science of The Total Environment.
  964. Khaki, M., Ait-El-Fquih, B., Hoteit, I., Forootan, E., Awange, J., Kuhn, M., (2017c). A Two-update Ensemble Kalman Filter for Land Hydrological Data Assimilation with an Un- certain Constraint. Journal of Hydrology, 555:447-462, doi:10.1016/j.jhydrol.2017.10.032.
  965. Khaki, M., Ait-El-Fquih, B., Hoteit, I., Forootan, E., Awange, J., Kuhn, M., (2018e). Un- supervised Ensemble Kalman Filtering with an Uncertain Constraint for Land Hydrological Data Assimilation. Revised and resubmitted to Journal of Hydrology.
  966. Mehdi Khaki I, as a Co-Author, endorse that this level of contributions by the candidate indicated above is appropriate. Ehsan Forootan Joseph L. Awange Michael Kuhn Ibrahim Hoteit Boujemaa Ait-El-Fquih