Surface roughness length dynamic over several different surfaces and its effects on modeling fluxes

Zhou, Yanlian; Sun, Xiaomin; Zhu, Zhenfeng; Zhang, Renhua; Tian, Jing; Liu, Yunfen; Guan, Dexin; Yuan, Guohui

doi:10.1007/s11430-006-8262-x

Cited by 34 publications

(33 citation statements)

References 12 publications

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“…Although our sensitivity analysis indicated that errors within the SEBS led to estimated H values with deviations (˘25%) of z 0m that were smaller than those for LST and air temperature, a 50% deviation in the parameterization of z 0m , as well as forest height, can generate errors of more than 40% for the H estimation. The result is consistent with those presented by van der Kwast et al [50], Zhou et al [75], and Ma et al [79]. In particular, according to the study of Zhou et al [71], the annual maximum effect of the roughness length dynamic on sensible heat flux was 33.80% and 18.11% for the Qianyanzhou (with a 11-12 meter high artificial needle forest) and Changbai Mountains (with a 26 meter high natural mixed forest) experimental stations, respectively.…”

Section: Discussionsupporting

confidence: 93%

“…Abiding by the theories of EB, bulk atmospheric similarity (BAS) [68] and MOS [39], based on a combination of physical land surface parameters obtained from remote sensing data and meteorological forcing data, the SEBS has been proven to be a reliable remote sensing-based ET model in numerous studies conducted over multiple ecosystems and under various climate and landscape conditions [69][70][71][72][73][74]. However, few studies have focused on forests [75][76][77], especially those located in cold and arid regions.…”

Section: The Surface Energy Balance System (Sebs)mentioning

confidence: 99%

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Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

Tian

Tol

et al. 2015

Remote Sensing

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Abstract:We propose a long-term parameterization scheme for two critical parameters, zero-plane displacement height (d) and aerodynamic roughness length (z 0m ), that we further use in the Surface Energy Balance System (SEBS). A sensitivity analysis of SEBS indicated that these two parameters largely impact the estimated sensible heat and latent heat fluxes. First, we calibrated regression relationships between measured forest vertical parameters (Lorey's height and the frontal area index (FAI)) and forest aboveground biomass (AGB). Next, we derived the interannual Lorey's height and FAI values from our calibrated regression models and corresponding forest AGB dynamics that were converted from interannual carbon fluxes, as simulated from two incorporated ecological models and a 2009 forest basis map These dynamic forest vertical parameters, combined with refined eight-day Global LAnd Surface Satellite (GLASS) LAI products, were applied to estimate the eight-day d, z 0m , and, thus, the heat roughness length (z 0h ). The obtained d, z 0m and z 0h were then used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration (ET) from 2000 to 2012 within the Qilian Mountains (QMs). As compared with MODIS, MOD16 products at the eddy covariance (EC) site, ET estimates from the SEBS agreed much better with EC measurements (R 2 = 0.80 and RMSE = 0.21 mm¨day´1).

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Section: Discussionsupporting

confidence: 93%

Section: The Surface Energy Balance System (Sebs)mentioning

confidence: 99%

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

Tian

Tol

et al. 2015

Remote Sensing

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“…However, z om has larger short-term fluctuations at QYZ than at CBS. At CBS, one of the major drivers of large seasonal variations in z om is the distinct seasonal pattern of the LAI, which increases from a minimum at the beginning of the growing season (early May) to a maximum in late June and starts to decrease in the middle of September (Zhou et al 2006). The seasonal pattern of the 5-day mean of z om closely follows that of the LAI.…”

Section: Final Determination Of D and Z Ommentioning

confidence: 76%

“…This parameter also depends on the surface-air temperature difference and atmospheric conditions. Therefore, z om is affected by the integrated effects of aerodynamic and thermodynamic factors and rough elements, which means that z om is actually a parameter varying with wind speed, wind direction, terrain, atmospheric stratification, and LAI (Zhang et al 2004;Patil 2006;Zhou et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Seasonal, Diurnal and Wind-Direction-Dependent Variations of the Aerodynamic Roughness Length in Two Typical Forest Ecosystems of China

Zhou¹,

Sun²,

Ju³

et al. 2012

Terr. Atmos. Ocean. Sci.

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Aerodynamic roughness length (z om ) is an important parameter for reliably simulating surface fluxes. The parameter varies with wind speed, atmospheric stratification, terrain and other factors; however, variations of this parameter are not properly considered in most models, which may result in uncertainties in simulating surface latent heat and sensible heat flux.There have been few studies of the diurnal and wind-direction dependent variations in z om . This study analyzes the seasonal, diurnal and wind-direction-dependent variations in z om calculated from the profile of meteorological data for two forest systems of China, and explores the mechanism underlying these variations.Annually averaged and monthly averaged diurnal variations in z om are obvious and similar at a Changbai Mountain (CBS) site located in northeast China and a Qianyanzhou (QYZ) site located in southeast China. z om is much higher at night than during the day. The diurnal variation in z om is due to a change in atmospheric stratification. The seasonality of z om differs at two sites. z om has sizable seasonal variation and is lower in the leaf-off season than in the growing season at CBS. At QYZ, z om does not have an obvious seasonal pattern. However, at QYZ, the short-term fluctuation is greater than that at CBS. The obvious seasonal variation in z om at CBS is related to the great seasonal change in the leaf area index. The noticeable short-term winddirection-dependent variations in z om at QYZ are attributed to the heterogeneous terrain there.

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“…L is used to determine the atmospheric stability near the surface [34]. When Z/L = 0, there is neutral stratification.…”

Section: Field Aerodynamic Roughness Length Calculationmentioning

confidence: 99%

Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

Xing

Niu

et al. 2018

Remote Sensing

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Abstract:The aerodynamic roughness length (z0m) is an important parameter that affects the momentum and energy exchange between the earth surface's and the atmosphere. In this paper, the gradient wind speed data that were observed from May to October, 2014, at the Si Daoqiao station, which is located in an area of sparse Tamarix in the downstream region of the Heihe River Basin (HRB), are used to evaluate the sensitivity of the moderate-resolution imaging spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) R, the MODIS/Landsat 8 normalized difference vegetation index (NDVI), and the wind speed at 5 m in comparison with the field-measured z0m. The results indicate that the NIR BRDF_R_MODIS and the NDVI MODIS /NDVI Landsat8 are less sensitive indicators of the z0m over sparse Tamarix areas (R 2 : 0.0045 for NIR BRDF_R MODIS ; R 2 : 0.0342 for NDVI MODIS ; and, R 2 : 0.1646 for NDVI Landsat8 ), which differs significantly from the results obtained by previous studies for farmlands and grasslands. However, there is a nearly linear correlation between the wind speed at 5 m and the z0m at the time scale of the NDVI Landsat8 acquisitions (R 2 : 0.3696). Furthermore, the combination of the NDVI and wind speed at 5 m can significantly improve this correlation (R 2 : 0.7682 for NDVI MODIS ; R 2 : 0.6304 for NDVI Landsat8 ), whereas the combination of the NIR BRDF_R MODIS and wind speed at 5 m still has a low correlation (R 2 : 0.0886). Finally, the regional z0m of the oasis in the downstream region of the HRB was determined using Landsat 8 surface reflectance data and the wind speed data at the Si Daoqiao station, which properly reflect the temporal evolution of the z0m in that region. The parameterization scheme proposed in this paper has great potential to be applied to evapotranspiration, land surface, and hydrologic model simulations of sparse Tamarix at Si Daoqiao site in the future.

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Surface roughness length dynamic over several different surfaces and its effects on modeling fluxes

Cited by 34 publications

References 12 publications

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

Seasonal, Diurnal and Wind-Direction-Dependent Variations of the Aerodynamic Roughness Length in Two Typical Forest Ecosystems of China

Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin

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