Continuous data assimilation for downscaling large-footprint soil moisture retrievals

Handle URI:
http://hdl.handle.net/10754/620980
Title:
Continuous data assimilation for downscaling large-footprint soil moisture retrievals
Authors:
Altaf, Muhammad; Jana, Raghavendra Belur ( 0000-0001-8113-1990 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Environmental Science and Engineering Program; Water Desalination & Reuse Centre; Biological, Environmental Sciences & Engineering Division
Citation:
Altaf MU, Jana RB, Hoteit I, McCabe MF (2016) Continuous data assimilation for downscaling large-footprint soil moisture retrievals. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII. Available: http://dx.doi.org/10.1117/12.2241042.
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Conference/Event name:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Issue Date:
2016 ; 25-Oct-2016
DOI:
10.1117/12.2241042
Type:
Conference Paper
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577851
Appears in Collections:
Conference Papers; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAltaf, Muhammaden
dc.contributor.authorJana, Raghavendra Beluren
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2016-10-13T12:33:28Z-
dc.date.available2016-10-13T12:33:28Z-
dc.date.issued2016-
dc.date.issued2016-10-25en
dc.identifier.citationAltaf MU, Jana RB, Hoteit I, McCabe MF (2016) Continuous data assimilation for downscaling large-footprint soil moisture retrievals. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII. Available: http://dx.doi.org/10.1117/12.2241042.en
dc.identifier.doi10.1117/12.2241042en
dc.identifier.urihttp://hdl.handle.net/10754/620980-
dc.description.abstractSoil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577851en
dc.rightsCopyright 2016 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en
dc.subjectCDAen
dc.subjectContinuous Data Assimilationen
dc.subjectModelingen
dc.subjectRemote Sensingen
dc.subjectScalingen
dc.subjectSoil Moistureen
dc.subjectVadose Zoneen
dc.titleContinuous data assimilation for downscaling large-footprint soil moisture retrievalsen
dc.typeConference Paperen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentWater Desalination & Reuse Centreen
dc.contributor.departmentBiological, Environmental Sciences & Engineering Divisionen
dc.identifier.journalRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIIen
dc.conference.date2016-09-26 to 2016-09-28en
dc.conference.nameRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIIen
dc.conference.locationEdinburgh, GBRen
dc.eprint.versionPost-printen
kaust.authorAltaf, Muhammaden
kaust.authorJana, Raghavendra Beluren
kaust.authorHoteit, Ibrahimen
kaust.authorMcCabe, Matthewen
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