Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes

Hydrology and Earth System Sciences

Volumn 19, Issue 4, 2015, Pages 1659-1676

  Alvarez Garreton, C ^a   Ryu, D ^a   Western, A W ^a   Su, C H ^a   Crow, W T ^b   Robertson, D E ^c   Leahy, C ^d  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c CSIRO   (Australia)

^d BUREAU OF METEOROLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CATCHMENTS; ERRORS; FLOODS; FORECASTING; MEAN SQUARE ERROR; METEOROLOGICAL INSTRUMENTS; MOISTURE; PROBABILITY DISTRIBUTIONS; RAIN; RUNOFF; SATELLITES; SOIL SURVEYS; SOILS; SPATIAL DISTRIBUTION; STREAM FLOW; SYSTEMATIC ERRORS;

ADVANCED MICROWAVE SCANNING RADIOMETER; CONTINUOUS RANKED PROBABILITY SCORES; RAINFALL-RUNOFF MODELS; REMOTELY SENSED SOIL MOISTURE; ROOT MEAN SQUARE ERRORS; SATELLITE SOIL MOISTURE; SEMI-DISTRIBUTED MODEL; SOIL MOISTURE AND OCEAN SALINITY (SMOS);

SOIL MOISTURE;

AMSR-E; ASCAT; CATCHMENT; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; FLOOD FORECASTING; RAINFALL; SATELLITE DATA; SEMIARID REGION; SMOS; SOIL MOISTURE; SOIL WATER; STREAMFLOW;

AUSTRALIA;

EID: 84927726216     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-19-1659-2015     Document Type: Article

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