A Spatial Evapotranspiration Tool at Grid Scale

Mylevaganam, Sivarajah; Ray, Chittaranjan

doi:10.4236/ojapps.2016.61007

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…This is owing to the fact that the classification system based on reference chemicals does not consider the acceptable levels of the pesticides at the groundwater table. 5) [13] has developed a spatial evapotranspiration tool (SET) that can geographically encompass all the best available climate datasets to estimate evapotranspiration (ET) using Penman-Monteith method [14] [15] at different spatial/grid scales. This spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPysite-package of ArcGIS.…”

Section: Future Researchmentioning

confidence: 99%

The Assessment of Groundwater Vulnerability Due to Leaching of Chemicals: The Review of Attenuation Factor

Mylevaganam¹,

Ray²

2016

OJSS

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To assess the groundwater vulnerability due to leaching of chemicals, the groundwater system in the unsaturated zone is characterized by conceptual models that are further extended and refined with more detailed mathematical models to understand the governing physical processes in detail. However, due to lack of data and uncertainty level, an intermediate transition through index based models is researched. The attenuation factor (AF) approach, which works under the assumption that the chemicals degrade following a first-order kinetics and determines the fraction of the chemicals that goes to groundwater table, is one of the index based models that has been widely used due to its simplicity. Therefore, the objective of this paper is to review the research works done using the AF approach, to outline the future research needs. Furthermore, the mathematical implementation of the AF approach and the associated uncertainty levels is explained through an example and MATLAB source code.

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Section: Future Researchmentioning

confidence: 99%

The Assessment of Groundwater Vulnerability Due to Leaching of Chemicals: The Review of Attenuation Factor

Mylevaganam¹,

Ray²

2016

OJSS

Self Cite

View full text Add to dashboard Cite

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“…The sample code to retrieve the datasets and the other inputs specified by the user, and the functionalities of the methods (e.g., get-ParameterInfo () and execute ()) within the "SpatialETSense" class are discussed in detail by [15]. To estimate the spatial ET using Penman-Monteith, this tool adopts the Python source code discussed by [15]. Therefore, only the important chunk of code relevant to sensitivity analysis is discussed in this paper.…”

Section: The Development Of Spatial Sensitivity Tool At Grid Scalementioning

confidence: 99%

“…InSolarR=Raster(inSolarRadiation)/Raster(inSolarRadiation)*(mulRange*x+inSenseLow) Having generated the raster dataset for the parameter that is tested in the sensitivity analysis, the ET raster is developed as discussed in Section 2.0. The detailed Python code is outlined by [15]. Since the simulation is carried for a specified number of times, the developed ET raster datasets are stored in a systematic way by appending the simulation number.…”

Section: The Development Of Spatial Sensitivity Tool At Grid Scalementioning

confidence: 99%

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

Mylevaganam¹,

Ray²

2016

JGIS

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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.

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A Spatial Evapotranspiration Tool at Grid Scale

Cited by 2 publications

References 4 publications

The Assessment of Groundwater Vulnerability Due to Leaching of Chemicals: The Review of Attenuation Factor

The Assessment of Groundwater Vulnerability Due to Leaching of Chemicals: The Review of Attenuation Factor

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

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