Location: Soil and Water Management Research
Title: Gaussian process models for reference ET estimation from alternative meteorological data sources Authors
|Holman, Daniel -|
|Sridharan, Mohan -|
|Porter, Dana -|
|Marek, Thomas -|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 1, 2014
Publication Date: June 5, 2014
Citation: (19)Holman, D., Sridharan, M., Gowda, P., Porter, D., Marek, T., Howell, T.A., Moorhead, J.E. 2014. Gaussian process models for reference ET estimation from alternative meteorological data sources. Journal of Hydrology. 517(19):28-35. Interpretive Summary: Accurate estimates of daily evapotranspiration (ET) rates for reference crop are needed for irrigation scheduling purposes. Due to lack of funding to support agriculture-based ET network in the Texas High Plains, there is a need for alternative data sources for estimating reference ET. In this study, a set of advanced Gaussian Process and ordinary least square regression models was developed to estimate daily reference ET from alternative data sources and compared for accuracy. Results indicated that Gaussian Process models provide much greater accuracy than ordinary least square regression models in estimating the reference ET.
Technical Abstract: Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management, especially in arid and semi-arid regions where crop water demand exceeds rainfall. Daily grass or alfalfa reference ET values and crop coefficients are widely used to estimate crop water demand. Inaccurate reference ET estimates can have a tremendous impact on irrigation costs and the demands on U.S. freshwater resources, particularly within the Ogallala Aquifer region. ET networks calculate reference ET using local meteorological data. Gaps in spatial coverage of existing networks and the agriculture-based Texas High Plains ET (TXHPET) network in jeopardy due to lack of funding, there is an immediate need for alternative sources capable of filling data gaps without high maintenance and field-based support costs. Non-agricultural weather stations located throughout the Texas High Plains are providing publicly accessible meteorological data. However, there are concerns that the data may not be suitable for estimating reference ET due to factors such as weather station siting, fetch requirements, data formats, parameters recorded, and quality control issues. The goal of the research reported in this paper is to assess the use of alternative data sources for reference ET computation. Towards this objective, we trained Gaussian process predictors, an instance of kernel-based machine learning algorithms, on data collected from weather stations to predict reference ET values and augment the TXHPET database. We show that Gaussian process models provide much greater accuracy than baseline least square regression models.