Valentijn R. N. Pauwels

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Peter A. Troch1,2* Guillermo F. Martinez1 Valentijn R. N. Pauwels3 Matej Durcik4 Murugesu Sivapalan5,6 Ciaran Harman6 Paul D. Brooks1,4 Hoshin Gupta1,4 and Travis Huxman7,2 1 Department of Hydrology and Water Resources, University of Arizona, USA 2 Biosphere 2 Earthscience, University of Arizona, USA 3 Laboratory of Hydrology and Water Management, Ghent(More)
Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in(More)
The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation(More)
The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g.(More)
It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical(More)
This paper describes and assesses the quality of the algorithm, “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR), developed in view of the forthcoming European Space Agency (ESA) Sentinel-1 (S1) mission. SMOSAR retrieves soil moisture (mv) products at high spatial resolution (i.e. less than 1km) from dense time series of either single (i.e.(More)
More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy,(More)
In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning(More)