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Estimating Daily Global Evapotranspiration
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Estimating Daily Global Evapotranspiration. |
| 개인저자 | Raoufi, Roozbeh. |
| 단체저자명 | Northeastern University. Civil and Environmental Engineering. |
| 발행사항 | [S.l.]: Northeastern University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 101 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781687938459 |
| 학위논문주기 | Thesis (Ph.D.)--Northeastern University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Beighley, Ed. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Evapotranspiration (ET) is a key component in the hydrological cycle, responsible for removing almost two thirds of all precipitation over land. Globally modeling ET is of great importance in determining near surface energy and water fluxes, especially in remote areas with limited available data. In the present study, 'actual' daily ET is directly modeled globally based on the Penman-Monteith equation using only remotely sensed data, eliminating the need for a hydrological model to determine the available moisture. Model estimates are validated against 39 Eddy Covariance Flux towers mostly in North America. It is shown that here, air temperature (Ta) and relative humidity (RH) are the main factors in estimating ET. In order to serve the global application of the model, daily Ta is estimated using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) product on the Aqua and Terra satellites. Other physical variables such as LAI, albedo, solar elevation angle, latitude, elevation, etc. are also used as auxiliary predictors in determining Ta. Several linear and non-linear regression methods are used, and it is shown that Artificial Neural Networks (ANN) can learn the most variabilities in the training data set and predict the targets most accurately. Similar to Ta, RH is estimated using remotely sensed data. Soil moisture from Soil Moisture Active Passive (SMAP) product is also used as a predictor in estimating RH. Again, several linear and non-linear regression methods are used, and it is shown that the most accurate method to estimate RH is Support Vector Regression (SVR). ET estimates will finally be modified using the improved air temperature and relative humidity. |
| 일반주제명 | Civil engineering. Hydrologic sciences. Water resources management. |
| 언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
