Gaussian process models for reference ET estimation from alternative meteorological data sources

Journal of Hydrology

Volumn 517, Issue , 2014, Pages 28-35

  Holman, Daniel ^a   Sridharan, Mohan ^b   Gowda, Prasanna ^c   Porter, Dana ^b,d   Marek, Thomas ^a   Howell, Terry ^c   Moorhead, Jerry ^c  

^a TEXAS A AND M UNIVERSITY   (United States)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

^d TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Advanced regression; Evapotranspiration; Supervised learning; Texas High Plains ET network

Indexed keywords

AGRICULTURAL MACHINERY; AQUIFERS; CROPS; EVAPOTRANSPIRATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IRRIGATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; METEOROLOGY; REGRESSION ANALYSIS; SUPERVISED LEARNING; WEATHER INFORMATION SERVICES;

ADVANCED REGRESSION; ARID AND SEMI-ARID REGIONS; CROP EVAPOTRANSPIRATION; FRESH WATER RESOURCES; GAUSSIAN PROCESS MODELS; IRRIGATION MANAGEMENT; LEAST SQUARE REGRESSION MODELS; PUBLICLY ACCESSIBLE; METEOROLOGICAL DATA;

COST BENEFIT ANALYSIS; EVAPOTRANSPIRATION;

ALFALFA; CROP; DATABASE; EVAPOTRANSPIRATION; GRASS; IRRIGATION; WATER DEMAND; WEATHER STATION; QUALITY CONTROL; RAINFALL;

OGALLALA AQUIFER; TEXAS; UNITED STATES;

EID: 84904873034     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.05.001     Document Type: Article

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