TITLE:
The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale
AUTHORS:
Sivarajah Mylevaganam, Chittaranjan Ray
KEYWORDS:
Evapotranspiration, Penman-Monteith Method, Aerodynamic Method, Energy Balance Method, Python, ArcPy, ArcGIS, Spatial Scale, Geoprocessing, Python Toolbox, Sensitivity Analysis, One-AT-A-Time, Sensitivity Index
JOURNAL NAME:
Journal of Geographic Information System,
Vol.8 No.1,
February
29,
2016
ABSTRACT: The need to allocate the
existing water in a sustainable manner, even with the projected population
growth, has made to assess the consumptive use or evapotranspiration (ET),
which determines the irrigation demand. As underscored in the literature,
Penman-Monteith method which is a combination of aerodynamic and energy balance
method is widely used and accepted as the method of estimation of ET. However,
the application of Penman-Monteith relies on many climate parameters such as
relative humidity, solar radiation, temperature, and wind speed. Therefore,
there exists a need to determine the parameters that are most sensitive and
correlated with dependent variable (i.e., ET), to strengthen the
knowledge base. However, the sensitivity of ET using Penman-Monteith is
oftentimes estimated using meteorological data from climate stations. Such
estimation of sensitivity may vary spatially and thus there exists a need to
estimate sensitivity of ET spatially. Thus, in this paper, based on
One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically
encompass all the best available climate datasets to produce ET and its
sensitivity at different spatial scales is developed. The spatial tool is
developed as a Python toolbox in ArcGIS using Python, an open source
programming language, and the ArcPy site-package of ArcGIS. The developed
spatial tool is demonstrated using the meteorological data from Automated
Weather Data Network in Nebraska in 2010. To summarize the outcome of the
sensitivity analysis using OAT method, sensitivity indices are developed for
each raster cell. The demonstration of the tool shows that, among the
considered parameters, the computed ET using Penman-Monteith is highly
sensitive to solar radiation followed by temperature for the state of Nebraska,
as depicted by the sensitivity index. The computed sensitivity index of wind
speed and the relative humidity are not that significant compared to the
sensitivity index of solar radiation and temperature.