HR: 12:05h
AN: H41H-08    [PDF]
TI: Surface Soil Moisture Assimilation From ASAR Imagery for Root Zone Moisture Predictions at Basin Scale
AU: * Caschili, A
EM: alessandro.caschili@polimi.it
AF: DIIAR,Politecnico di Milano, Piazza Leonardo da Vinci, 32, MILANO, 20133 Italy
AU: Montaldo, N
EM: nicola.montaldo@polimi.it
AF: DIIAR,Politecnico di Milano, Piazza Leonardo da Vinci, 32, MILANO, 20133 Italy
AU: Mancini, M
EM: marco.mancini@polimi.it
AF: DIIAR,Politecnico di Milano, Piazza Leonardo da Vinci, 32, MILANO, 20133 Italy
AU: Albertson, J D
EM: john.albertson@duke.edu
AF: Department of Civil and Environmental Engineering, Duke University, Box 90287; Hudson Hall, Durham, NC 27708-0287 United States
AU: Botti, P
EM: paobotti@tin.it
AF: Ente Autonomo del Flumendosa, via Mameli, 88, CAGLIARI, 09100 Italy
AU: Dessena, M A
EM: mdessena@tiscalinet.it
AF: Ente Autonomo del Flumendosa, via Mameli, 88, CAGLIARI, 09100 Italy
AU: Carboni, E
EM: epiga@unica.it
AF: DIT, Universit… di Cagliari, Piazza d'Armi, CAGLIARI, 09100 Italy
AB: The state of the root-zone soil moisture is a key variable controlling surface water and energy balances. Emerging efforts in data assimilation seek to guide land surface models (LSMs) with periodic observations of surface soil moisture. Montaldo et al. (Water Resour. Res., 2001) and Montaldo and Albertson (Adv. Water Resour., 2003) developed an operational multi-scale assimilation system for robust root zone soil moisture predictions at the local scale. The assimilation scheme, developed for a force-restore method based LSM, updates the measured surface soil moisture, the root zone soil water content and the soil hydraulic conductivity, in a manner that compensates for both inaccurate initial conditions and model parameter estimates. In this presentation we describe the development and testing of an operational assimilation system for robust root-zone soil moisture predictions at the basin scale. High resolution data of the new ASAR (advanced synthetic aperture radar) sensor aboard European Space Agency's Envisat satellite offers the opportunity for monitoring surface soil moisture at high resolution (up to 30 m), which is suitable for distributed mapping within the small scales of typical Mediterranean basins. Indeed, adequate spatio-temporal monitoring of the soil moisture is essential to improve our capability to simulate the water balance. As part of a recently-approved European Space Agency (ESA) Envisat AO project, ASAR-based soil moisture mapping of the Mulargia basin (area of about 65 sq.km), sub-basin of the Flumendosa basin in Sardinia, are available . This semi-arid basin has a key role in the water resources management of Sardinia. Semi-arid regions, such as Sardinia island, suffers from water scarcity, which is increasingly due to the broad desertification processes of the Mediterranean area. Within the basin, land surface fluxes are well monitored through two evapotraspiration measurement systems (one eddy correlation technique based station, and one Bowen ratio station), and spatially distributed soil moisture ground-truth data needed to assess the ASAR imagery are collected over the whole basin through the TDR technique and the gravimetric method. The objectives of this work are to: 1) test the high resolution ASAR imagery accuracy for producing maps of surface soil moisture patterns at the catchment scale, 2) develop and test an operational assimilation system for robust root-zone soil moisture predictions at the basin scale. The developed assimilation system will have two components, 1) ASAR observations will be merged with the model for robust surface soil moisture estimates though the Ensemble Kalman Filter, and 2) the surface soil moisture estimates will then drive an assimilation engine for robust root-zone soil moisture predictions.
DE: 1836 Hydrologic budget (1655)
DE: 1866 Soil moisture
DE: 1875 Unsaturated zone
DE: 3337 Numerical modeling and data assimilation
DE: 3360 Remote sensing
SC: Hydrology [H]
MN: 2003 Fall Meeting