A Two-Source Model for Estimating Evaporative Fraction (TMEF) Coupling Priestley-Taylor Formula and Two-Stage Trapezoid

A Two-Source Model for Estimating Evaporative Fraction (TMEF) Coupling Priestley-Taylor Formula and Two-Stage Trapezoid
复制标题

耦合 Priestley-Taylor 公式和两级梯形的估计蒸发分数 (TMEF) 的双源模型

DOI:
10.3390/rs8030248
复制
发表时间:
2016-03
期刊:
影响因子:
5
通讯作者:
Sun Hao
Sun Hao
中科院分区:
工程技术2区
文献类型:
--
作者:
Sun Hao

文献摘要

参考文献

被引文献

相似文献

遥感地表温度和植被覆盖度(LST/FVC)空间已被广泛应用于地表蒸发量(EF)的模拟和分区,这在水资源管理中具有重要意义。然而,大多数这样的模型是基于传统的梯形,并简单地确定湿边缘作为空气温度(Ta)或图像中的最低LST值。我们发展了一个新的双源模型,用于估计EF(TMEF)的基础上,两个阶段的梯形耦合与Priestly-Taylor公式的扩展。湿边缘上的潜热通量用Priestly-Taylor公式计算,而干边缘上的潜热通量设为0。然后通过求解辐射收支和能量平衡方程来确定湿边缘和干边缘。通过与其他两种基于传统梯形(即,两源梯形蒸散量模型(TTME)和一源梯形EF模型(OTEF))在2012年使用MODIS产品和HiWATER-MUSOEXE的实地观测模拟和划分EF的效果。结果表明,TMEF模型优于其他两个模型,其中EF平均绝对相对偏差为9.57%(TMEF),15.03%(TTME)和30.49%(OTEF)。
Remotely sensed land surface temperature and fractional vegetation coverage (LST/FVC) space has been widely used in modeling and partitioning land surface evaporative fraction (EF) which is important in managing water resources. However, most of such models are based on conventional trapezoid and simply determine the wet edge as air temperature (Ta) or the lowest LST value in an image. We develop a new Two-source Model for estimating EF (TMEF) based on a two-stage trapezoid coupling with an extension of the Priestly-Taylor formula. Latent heat flux on the wet edge is calculated with the Priestly-Taylor formula, whereas that on the dry edge is set to 0. The wet and dry edges are then determined by solving radiation budget and energy balance equations. The model was evaluated by comparing with other two models that based on conventional trapezoid (i.e., the Two-source Trapezoid Model for Evapotranspiration (TTME) and a One-source Trapezoid model for EF (OTEF)) in how well they simulate and partition EF using MODIS products and field observations from HiWATER-MUSOEXE in 2012. Results show that the TMEF outperforms the other two models, where EF mean absolute relative deviations are 9.57% (TMEF), 15.03% (TTME), and 30.49% (OTEF).
DOI: 10.1080/01431160500121727
发表时间: 2006-05-01
影响因子: 3.4
作者:
Karnieli, A;Bayasgalan, M;Tucker, CJ
通讯作者: Tucker, CJ
DOI: 10.1029/1999gl006049
发表时间: 1999-09
影响因子: 5.2
作者:
Le Jiang;S. Islam
通讯作者: Le Jiang;S. Islam
DOI: 10.1175/2009jcli2900.1
发表时间: 2010-02-01
期刊: JOURNAL OF CLIMATE
影响因子: 4.9
作者:
Karnieli, Arnon;Agam, Nurit;Goldberg, Alexander
通讯作者: Goldberg, Alexander
DOI: 10.1002/2013wr014581
发表时间: 2014-02-01
影响因子: 5.4
作者:
Long, Di;Longuevergne, Laurent;Scanlon, Bridget R.
通讯作者: Scanlon, Bridget R.
DOI: 10.1016/s0034-4257(01)00274-7
发表时间: 2002-02-01
影响因子: 13.5
作者:
Sandholt, I;Rasmussen, K;Andersen, J
通讯作者: Andersen, J