| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435216 |
| 005 | | 20200227163406 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687938459 |
| 035 | |
▼a (MiAaPQ)AAI22622608 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 333 |
| 100 | 1 |
▼a Raoufi, Roozbeh. |
| 245 | 10 |
▼a Estimating Daily Global Evapotranspiration. |
| 260 | |
▼a [S.l.]:
▼b Northeastern University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 101 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: B. |
| 500 | |
▼a Advisor: Beighley, Ed. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Northeastern University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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. |
| 590 | |
▼a School code: 0160. |
| 650 | 4 |
▼a Civil engineering. |
| 650 | 4 |
▼a Hydrologic sciences. |
| 650 | 4 |
▼a Water resources management. |
| 690 | |
▼a 0543 |
| 690 | |
▼a 0388 |
| 690 | |
▼a 0595 |
| 710 | 20 |
▼a Northeastern University.
▼b Civil and Environmental Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-05B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0160 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493911
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |