Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling

Advances in Water Resources

Volumn 94, Issue , 2016, Pages 364-378

  Yan, Hongxiang ^a   Moradkhani, Hamid ^a  

^a Portland State University   (United States)

Author keywords

ASCAT; Data assimilation; Geostatistical modeling; Markov chain Monte Carlo; Particle filter; Soil moisture

Indexed keywords

BANDPASS FILTERS; CHAINS; MARKOV PROCESSES; METEOROLOGICAL INSTRUMENTS; MOISTURE; MONTE CARLO METHODS; REMOTE SENSING; SATELLITES; SOIL SURVEYS; SOILS; STREAM FLOW;

ASCAT; DATA ASSIMILATION; GEOSTATISTICAL MODELING; MARKOV CHAIN MONTE-CARLO; PARTICLE FILTER;

SOIL MOISTURE;

ASCAT; DATA ASSIMILATION; GEOSTATISTICS; HYDROLOGICAL MODELING; MARKOV CHAIN; MONTE CARLO ANALYSIS; REMOTE SENSING; SOIL MOISTURE; STREAMFLOW;

EID: 84974627083     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2016.06.002     Document Type: Article

References (87)

1. Abaza M., Anctil F., Fortin V., Turcotte R. Sequential streamflow assimilation for short-term hydrological ensemble forecasting. J. Hydrol. 2014, 519:2692-2706. 10.1016/j.jhydrol.2014.08.038.
2. Alvarez-Garreton C., Ryu D., Western A.W., Crow W.T., Robertson D.E. The impacts of assimilating satellite soil moisture into a rainfall-runoff model in a semi-arid catchment. J. Hydrol. 2014, 519:2763-2774. 10.1016/j.jhydrol.2014.07.041.
3. Alvarez-Garreton C., Ryu D., Western A.W., Su C.-H., Crow W.T., Robertson D.E., et al. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrol. Earth Syst. Sci. Discuss. 2014, 11:10635-10681. 10.5194/hessd-11-10635-2014.
4. Andrieu C., Doucet A., Holenstein R. Particle Markov chain Monte Carlo methods. J. R. Stat. Soc. Ser. B. Stat. Methodol.) 2010, 72:269-342. 10.1111/j.1467-9868.2009.00736.x.
5. Aubert D., Loumagne C., Oudin L. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J. Hydrol. 2003, 280:145-161.
6. Barbu A.L., Calvet J.-C., Mahfouf J.-F., Lafont S. Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France. Hydrol. Earth Syst. Sci. 2014, 18:173-192.
7. Barry D.A., Bajracharya K. On the Muskingum-Cunge flood routing method. Environ. Int. 1995, 21:485-490.
8. Bartalis Z., Wagner W., Naeimi V., Hasenauer S., Scipal K., Bonekamp H., et al. Initial soil moisture retrievals from the METOP-A advanced scatterometer (ASCAT). Geophys. Res. Lett. 2007, 34. 10.1029/2007GL031088.
9. Brocca L., Melone F., Moramarco T., Wagner W., Naeimi V., Bartalis Z., et al. Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrol. Earth Syst. Sci. 2010, 14:1881-1893. 10.5194/hess-14-1881-2010.
10. Brocca L., Moramarco T., Melone F., Wagner W., Hasenauer S., Hahn S. Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall-runoff modeling. IEEE Trans. Geosci. Remote Sens. 2012, 50:2542-2555. 10.1109/TGRS.2011.2177468.
11. Burnash, R.J.C., Ferral, R.L., and McGuire, R.A., 1973. A generalized streamflow simulation system: Conceptual modeling for digital computers, US Dept. of Commerce, National Weather Service.
12. Chen F., Crow W.T., Starks P.J., Moriasi D.N. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Adv. Water Resour. 2011, 34:526-536. 10.1016/j.advwatres.2011.01.011.
13. Chen W., Huang C., Shen H., Li X. Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation. Adv. Water Resour. 2015, 10.1016/j.advwatres.2015.08.003.
14. Crow W.T., Ryu D. A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals. Hydrol. Earth Syst. Sci. 2009, 13:1-16. 10.5194/hess-13-1-2009.
15. De Lannoy G.J.M., Reichle R.H., Arsenault K.R., Houser P.R., Kumar S., Verhoest N.E.C., et al. Multiscale assimilation of advanced microwave scanning radiometer-EOS snow water equivalent and moderate resolution imaging spectroradiometer snow cover fraction observations in northern Colorado. Water Resour. Res. 2012, 48. 10.1029/2011WR010588.
16. Dechant C.M., Moradkhani H. Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation. Hydrol. Earth Syst. Sci. 2011, 15:3399-3410. 10.5194/hess-15-3399-2011.
17. Dechant C., Moradkhani H. Radiance data assimilation for operational snow and streamflow forecasting. Adv. Water Resour. 2011, 34:351-364. 10.1016/j.advwatres.2010.12.009.
18. Dechant C.M., Moradkhani H. Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting. Water Resour. Res. 2012, 48:1-15. 10.1029/2011WR011011.
19. Dong J., Steele-Dunne S.C., Judge J., van de Giesen N. A particle batch smoother for soil moisture estimation using soil temperature observations. Adv. Water Resour. 2015, 83:111-122. 10.1016/j.advwatres.2015.05.017.
20. Draper C.S., Reichle R.H., De Lannoy G.J.M., Liu Q. Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett. 2012, 39. 10.1029/2011GL050655.
21. Duan Q., Schaake J., Andréassian V., Franks S., Goteti G., Gupta H.V., et al. Model parameter estimation experiment (MOPEX): an overview of science strategy and major results from the second and third workshops. J. Hydrol. 2006, 320:3-17. 10.1016/j.jhydrol.2005.07.031.
22. Entekhabi D., Njoku E.G., O'Neill P.E., Kellogg K.H., Crow W.T., Edelstein W.N., et al. The soil moisture active passive (SMAP) mission. Proc. IEEE 2010, 98:704-716. 10.1109/JPROC.2010.2043918.
23. Hamill T.M. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Weather Rev. 2001, 129:550-560. 10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.
24. Han X., Franssen H.J.H., Montzka C., Vereecken H. Soil moisture and soil properties estimation in the community land model with synthetic brightness temperature observations. Water Resour. Res. 2014, 50:6081-6105. 10.1002/2013WR014586.
25. Han X., Li X., Rigon R., Jin R., Endrizzi S. Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization. PLoS One 2015, 10. 10.1371/journal.pone.0116435.
26. Jackson T.J., Cosh M.H., Bindlish R., Starks P.J., Bosch D.D., Seyfried M., et al. Validation of advanced microwave scanning radiometer soil moisture products. IEEE Trans. Geosci. Remote Sens. 2010, 48:4256-4272. 10.1109/TGRS.2010.2051035.
27. Kerr Y.H., Waldteufel P., Wigneron J.-P., Delwart S., Cabot F., Boutin J., et al. The SMOS Mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE 2010, 98. 10.1109/JPROC.2010.2043032.
28. Kumar S.V., Peters-Lidard C.D., Mocko D., Reichle R., Liu Y., Arsenault K.R., et al. Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J. Hydrometeorol 2014, 15:2446-2469. 10.1175/JHM-D-13-0132.1.
29. Kumar S.V., Harrison K.W., Peters-Lidard C.D., Santanello J.a., Kirschbaum D. Assessing the impact of L-band observations on drought and flood risk estimation: a decision theoretic approach in an OSSE environment. J. Hydrometeorol 2014, 10.1175/JHM-D-13-0204.1.
30. Kumar S.V., Peters-Lidard C.D., Santanello J.a., Reichle R.H., Draper C.S., Koster R.D., et al. Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes. Hydrol. Earth Syst. Sci. Discuss. 2015, 12:5967-6009. 10.5194/hessd-12-5967-2015.
31. Lee H., Seo D.J., Koren V. Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: effects of uncertainties in the data and initial model soil moisture states. Adv. Water Resour. 2011, 34:1597-1615. 10.1016/j.advwatres.2011.08.012.
32. Leisenring M., Moradkhani H. Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation. J. Hydrol. 2012, 468-469:268-282. 10.1016/j.jhydrol.2012.08.049.
33. Leung S., Cooley D. A comparison of a traditional geostatistical regression approach and a general Gaussian process approach for spatial prediction. Stat 2014, 3:228-239. 10.1002/sta4.57.
34. Liu Q., Reichle R.H., Bindlish R., Cosh M.H., Crow W.T., de Jeu R., et al. The Contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeorol. 2011, 12:750-765. 10.1175/JHM-D-10-05000.1.
35. Liu Y., Weerts a H., Clark M., Hendricks Franssen H.J., Kumar S., Moradkhani H., et al. Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci. 2012, 16:3863-3887. 10.5194/hess-16-3863-2012.
36. Massari C., Brocca L., Tarpanelli A., Moramarco T. Data assimilation of satellite soil moisture into rainfall-runoff modelling: a complex recipe?. Remote Sens. 2015, 7:11403-11433. 10.3390/rs70911403.
37. Milly P.C.D., Betancourt J., Falkenmark M., Hirsch R.M., Kundzewicz Z.W., Lettenmaier D.P., et al. Climate change. Stationarity is dead: whither water management?. Science 2008, 319:573-574. 10.1126/science.1151915.
38. Montzka C., Moradkhani H., Weihermüller L., Franssen H.-J.H., Canty M., Vereecken H. Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter. J. Hydrol. 2011, 399:410-421. 10.1016/j.jhydrol.2011.01.020.
39. Montzka C., Pauwels V.R.N., Franssen H.J.H., Han X., Vereecken H. Multivariate and multiscale data assimilation in terrestrial systems: a review. Sensors (Switzerland) 2012, 12:16291-16333. 10.3390/s121216291.
40. Montzka C., Grant J.P., Moradkhani H., Franssen H-J.H., Weihermüller L., Drusch M., et al. Estimation of radiative transfer parameters from L-band passive microwave brightness temperatures using advanced data assimilation. Vadose Zo J 2013, 12:1-17. 10.2136/vzj2012.0040.
41. Moradkhani H., Hsu K.-L., Gupta H., Sorooshian S. Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resour. Res. 2005, 41:W05012. 10.1029/2004WR003604.
42. Moradkhani H., Sorooshian S., Gupta H.V., Houser P.R. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour. 2005, 28:135-147. 10.1016/j.advwatres.2004.09.002.
43. Moradkhani H., Dechant C.M., Sorooshian S. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method. Water Resour. Res. 2012, 48:W12520. 10.1029/2012WR012144.
44. Moradkhani H. Hydrologic remote sensing and land surface data assimilation. Sensors 2008, 8:2986-3004. 10.3390/s8052986.
45. Mu Q., Zhao M., Running S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 2011, 115:1781-1800. 10.1016/j.rse.2011.02.019.
46. Najafi M.R., Moradkhani H. Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J. Hydrol. 2015, 525:352-361. 10.1016/j.jhydrol.2015.03.045.
47. Njoku E.G., Jackson T.J., Lakshmi V., Chan T.K., Nghiem S.V. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote. Sens. 2003, 41:215-228. 10.1109/TGRS.2002.808243.
48. Pan M., Wood E.F. Inverse streamflow routing. Hydrol. Earth Syst. Sci. 2013, 17:4577-4588. 10.5194/hess-17-4577-2013.
49. Pan M., Wood E.F., McLaughlin D.B., Entekhabi D., Luo L. A multiscale ensemble filtering system for hydrologic data assimilation. part i: implementation and synthetic experiment. J. Hydrometeorol. 2009, 10:794-806. 10.1175/2009JHM1088.1.
50. Parada L.M., Liang X. Optimal multiscale Kalman filter for assimilation of near-surface soil moisture into land surface models. J. Geophys. Res. D. Atmos. 2004, 109:1-21. 10.1029/2004JD004745.
51. Parajka J., Naeimi V., Blöschl G., Wagner W., Merz R., Scipal K. Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrol. Earth Syst. Sci. Discuss. 2006, 10:353-368.
52. Parrish M.A., Moradkhani H., Dechant C.M. Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation. Water Resour. Res. 2012, 48. 10.1029/2011WR011116.
53. Pathiraja S., Marshall L., Sharma A., Moradkhani H. Hydrologic modeling in dynamic catchments: a data assimilation approach. Water Resour. Res. 2016, 10.1002/2015WR017192.
54. Pathiraja S., Marshall L., Sharma A., Moradkhani H. Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation. Adv. Water Resour. 2016, 94:103-119. 10.1016/j.advwatres.2016.04.021.
55. Plaza D.A., De Keyser R., De Lannoy G.J.M., Giustarini L., Matgen P., Pauwels V.R.N. The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter. Hydrol. Earth Syst. Sci. 2012, 16:375-390. 10.5194/hess-16-375-2012s.
56. Reichle R.H., Koster R.D. Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett. 2004, 31. 10.1029/2004GL020938.
57. Reichle R.H., McLaughlin D.B., Entekhabi D. Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications. IEEE Trans. Geosci. Remote Sens. 2001, 39:1708-1718. 10.1109/36.942549.
58. Reichle R., McLaughlin D.B., Entekhabi D. Hydrologic data assimilation with the ensemble Kalman filter. Mon. Weather. Rev. 2002, 130:103-114. 10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2.
59. Reichle R.H., De Lannoy G.J.M., Forman B.A., Draper C.S., Liu Q. Connecting satellite observations with water cycle variables through land data assimilation: examples using the NASA GEOS-5 LDAS. Surv. Geophys. 2014, 35:577-606. 10.1007/s10712-013-9220-8.
60. Reichle R.H. Data assimilation methods in the earth sciences. Adv. Water Resour. 2008, 31:1411-1418. 10.1016/j.advwatres.2008.01.001.
61. Ridler M.-E., Madsen H., Stisen S., Bircher S., Fensholt R. Assimilation of SMOS-derived soil moisture in a fully integrated hydrological and soil-vegetation-atmosphere transfer model in Western Denmark. Water Resour. Res. 2014, 50:8962-8981.
62. Rosolem R., Hoar T., Arellano A., Anderson J.L., Shuttleworth W.J., Zeng X., et al. Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale. Hydrol. Earth Syst. Sci. 2014, 18:4363-4379. 10.5194/hess-18-4363-2014.
63. Sahoo A.K., De Lannoy G.J.M., Reichle R.H., Houser P.R. Assimilation and downscaling of satellite observed soil moisture over the little river experimental watershed in Georgia, USA. Adv. Water Resour. 2013, 52:19-33. 10.1016/j.advwatres.2012.08.007.
64. Samaniego L., Kumar R., Zink M. Implications of parameter uncertainty on soil moisture drought analysis in Germany. J. Hydrometeorol. 2013, 14:47-68. 10.1175/JHM-D-12-075.1.
65. Samuel J., Coulibaly P., Dumedah G., Moradkhani H. Assessing model state and forecasts variation in hydrologic data assimilation. J. Hydrol. 2014, 513:127-141. 10.1016/j.jhydrol.2014.03.048.
66. Schaefer G.L., Cosh M.H., Jackson T.J. The USDA natural resources conservation service soil climate analysis network (SCAN). J. Atmos. Ocean Technol. 2007, 24:2073-2077. 10.1175/2007JTECHA930.1.
67. Scipal K., Scheffler C., Wagner W. Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. Hydrol. Earth Syst. Sci. Discuss. 2005, 2:417-448. 10.5194/hessd-2-417-2005.
68. Seneviratne S.I., Corti T., Davin E.L., Hirschi M., Jaeger E.B., Lehner I., et al. Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev. 2010, 99:125-161. 10.1016/j.earscirev.2010.02.004.
69. Seo D.-J., Koren V., Cajina N. Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting. J. Hydrometeorol. 2003, 4:627-641. 10.1175/1525-7541(2003)004<0627:RVAOHA>2.0.CO;2.
70. Shuttleworth J., Rosolem R., Zreda M., Franz T. The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation. Hydrol. Earth Syst. Sci. 2013, 17:3205-3217. 10.5194/hess-17-3205-2013.
71. Su C.H., Ryu D., Crow W.T., Western A.W. Beyond triple collocation: applications to soil moisture monitoring. J. Geophys. Res. Atmos. 2014, 119:6419-6439. 10.1002/2013JD021043.
72. Vrugt J.a., ter Braak C.J.F., Diks C.G.H., Schoups G. Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: theory, concepts and applications. Adv. Water Resour. 2013, 51:457-478. 10.1016/j.advwatres.2012.04.002.
73. Wagner W., Hahn S., Kidd R., Melzer T., Bartalis Z., Hasenauer S., et al. The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications. Meteorol Zeitschrift 2013, 22:5-33. 10.1127/0941-2948/2013/0399.
74. Wanders N., Karssenberg D., De Roo A., De Jong S.M., Bierkens M.F.P. The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol. Earth Syst. Sci. 2014, 18:2343-2357. 10.5194/hess-18-2343-2014.
75. Wang A., Lettenmaier D.P., Sheffield J. Soil moisture drought in China, 1950-2006. J. Clim. 2011, 24:3257-3271. 10.1175/2011JCLI3733.1.
76. Wood E.F., Schubert S.D., Wood A.W., Peters-Lidard C.D., Mo K.C., Mariotti A., et al. Prospects for advancing drought understanding, monitoring, and prediction. J. Hydrometeorol. 2015, 16:1636-1657. 10.1175/JHM-D-14-0164.1.
77. Xia Y., Mitchell K., Ek M., Sheffield J., Cosgrove B., Wood E., et al. Continental-scale water and energy flux analysis and validation for the north American land data assimilation system project phase 2 (NLDAS-2): 1. intercomparison and application of model products. J. Geophys. Res. 2012, 117. 10.1029/2011JD016048.
78. Yan H., Edwards F.G. Effects of land use change on hydrologic response at a watershed scale, Arkansas. J. Hydrol. Eng. 2013, 18:1779-1785. 10.1061/(ASCE)HE.1943-5584.0000743.
79. Yan H., Moradkhani H. Bayesian model averaging for flood frequency analysis. World Environ. Water Resour. Congr. 2014, 2014:1886-1895. 10.1061/9780784413548.189.
80. Yan H., Moradkhani H. A regional Bayesian hierarchical model for flood frequency analysis. Stoch. Environ. Res. Risk Assess. 2015, 29:1019-1036. 10.1007/s00477-014-0975-3.
81. Yan H., Moradkhani H. Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling. Nat. Hazard. 2016, 81:203-225. 10.1007/s11069-015-2070-6.
82. Yan H., DeChant C.M., Moradkhani H. Improving soil moisture profile prediction with the particle filter-Markov chain Monte Carlo method. IEEE Trans. Geosci. Remote Sens. 2015, 53:6134-6147. 10.1109/TGRS.2015.2432067.
83. Yan H. Magnitude and frequency of floods for rural, unregulated streams of Tennessee by L-Moments method 2012, University of Arkansas.
84. Yilmaz M.T., Crow W.T. The optimality of potential rescaling approaches in land data assimilation. J. Hydrometeorol. 2013, 650-660. 10.1175/JHM-D-12-052.1.
85. Yin J., Zhan X., Zheng Y., Liu J., Hain C.R., Fang L. Impact of quality control of satellite soil moisture data on their assimilation into land surface model. Geophys. Res. Lett. 2014, 41:7159-7166. 10.1002/2014GL060659.
86. Yuan X., Ma Z., Pan M., Shi C. Microwave remote sensing of short-term droughts during crop growing seasons. Geophys. Res. Lett. 2015, 42:4394-4401. 10.1002/2015GL064125.
87. Zhuo L., Han D. Misrepresentation and amendment of soil moisture in conceptual hydrological modelling. J. Hydrol. 2016, 535:637-651. 10.1016/j.jhydrol.2016.02.033.