Continuous data assimilation for downscaling large-footprint soil moisture retrievals

Handle URI:
http://hdl.handle.net/10754/620979
Title:
Continuous data assimilation for downscaling large-footprint soil moisture retrievals
Authors:
Altaf, M. U.; Jana, Raghavendra B. ( 0000-0001-8113-1990 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; McCabe, Matthew F. ( 0000-0002-1279-5272 )
Abstract:
Soil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly or indirectly. Better characterization of the spatial variability of soil moisture leads to better predictions from hydrologic/climate models. In-situ measurements have fine resolution, but become impractical in terms of coverage over large extents. Remotely sensed data have excellent spatial coverage extents, but suffer from poorer spatial and temporal resolution. We present here an innovative approach to downscaling coarse resolution soil moisture data by combining data assimilation and physically based modeling. In this approach, we exploit the features of Continuous Data Assimilation (CDA). 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 (e.g., HYDRUS) are subjected to data assimilation conditioned upon the 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. The large scale features of the model output are constrained to the observations, and as a consequence, the misfit at the fine scale is reduced. The advantage of this approach is that fine resolution soil moisture maps can be generated across large spatial extents, given the coarse resolution data. The data assimilation approach also enables multi-scale data generation which is helpful to match the soil moisture input data to the corresponding modeling scale. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometer-based, coarse resolution product from NASA’s SMAP satellite.
KAUST Department:
Water Desalination & Reuse Research Cntr; Earth Science and Engineering Program
Conference/Event name:
SPIE Remote Sensing
Issue Date:
Sep-2016
Type:
Poster
Additional Links:
https://spie.org/ERS/conferencedetails/remote-sensing-agriculture-ecosystems-hydrology
Appears in Collections:
Posters; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorAltaf, M. U.en
dc.contributor.authorJana, Raghavendra B.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorMcCabe, Matthew F.en
dc.date.accessioned2016-10-13T12:21:50Z-
dc.date.available2016-10-13T12:21:50Z-
dc.date.issued2016-09-
dc.identifier.urihttp://hdl.handle.net/10754/620979-
dc.description.abstractSoil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly or indirectly. Better characterization of the spatial variability of soil moisture leads to better predictions from hydrologic/climate models. In-situ measurements have fine resolution, but become impractical in terms of coverage over large extents. Remotely sensed data have excellent spatial coverage extents, but suffer from poorer spatial and temporal resolution. We present here an innovative approach to downscaling coarse resolution soil moisture data by combining data assimilation and physically based modeling. In this approach, we exploit the features of Continuous Data Assimilation (CDA). 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 (e.g., HYDRUS) are subjected to data assimilation conditioned upon the 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. The large scale features of the model output are constrained to the observations, and as a consequence, the misfit at the fine scale is reduced. The advantage of this approach is that fine resolution soil moisture maps can be generated across large spatial extents, given the coarse resolution data. The data assimilation approach also enables multi-scale data generation which is helpful to match the soil moisture input data to the corresponding modeling scale. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometer-based, coarse resolution product from NASA’s SMAP satellite.en
dc.relation.urlhttps://spie.org/ERS/conferencedetails/remote-sensing-agriculture-ecosystems-hydrologyen
dc.subjectData Assimilationen
dc.subjectSoil moistureen
dc.titleContinuous data assimilation for downscaling large-footprint soil moisture retrievalsen
dc.typePosteren
dc.contributor.departmentWater Desalination & Reuse Research Cntren
dc.contributor.departmentEarth Science and Engineering Programen
dc.conference.date26-29 September, 2016en
dc.conference.nameSPIE Remote Sensingen
dc.conference.locationEdinburgh, UKen
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