

ORIGINAL ARTICLE 

Year : 2016  Volume
: 39
 Issue : 1  Page : 4447 


Estimation of roughness length Z_{0} for Kalpakkam site
T Jesan, C Manonmani, J Thulasi Brindha, S Rajaram, PM Ravi, RM Tripathi
Health Physics Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra, India
Date of Web Publication  1Jul2016 
Correspondence Address: T Jesan Health Physics Division, Bhabha Atomic Research Centre, Mumbai  400 085, Maharashtra India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09720464.185182
The accurate knowledge of the surface topographic parameters, which represent terrain characteristics of a site, is required in atmospheric dispersion modeling. The rough surface retards the wind flow from a smooth surface, which results in sharp decrease in wind speed, change in vertical profile along with atmospheric stability. Dispersion models require extrapolation of wind speed U_{Ref} at measured height Z_{Ref} to the wind speed U_{Stack} at stack height Z_{Stack} , based on power law wind profile. Wind speed profile can be evaluated using site specific surface scaling parameters roughness length Z_{0} and friction velocity U_{*} . In this paper, sector dependent Z_{0} and U_{*} for Kalpakkam site is evaluated for neutral category and the sector values of Z_{0} are explained according to the terrain features. The maximum and minimum Z_{0} was observed in SSE (0.84 m) and N (0.26 m) upwind sectors due to the presence of more number of residential buildings, sand dunes, and high traffic, in contrast to N sector where mostly agricultural farming lands are present. The site specific wind speed profile index P is calculated based on Z_{0} for different sectors leads to better estimation of annual dilution factor and gamma dose on the part of neutral category computation. The surface scaling parameter mean surface drag coefficient C_{D} was estimated to be 0.16, which implies urban environment at Kalpakkam site. Keywords: Atmospheric dispersion modeling, atmospheric stability, roughness length
How to cite this article: Jesan T, Manonmani C, Brindha J T, Rajaram S, Ravi P M, Tripathi R M. Estimation of roughness length Z_{0} for Kalpakkam site. Radiat Prot Environ 2016;39:447 
How to cite this URL: Jesan T, Manonmani C, Brindha J T, Rajaram S, Ravi P M, Tripathi R M. Estimation of roughness length Z_{0} for Kalpakkam site. Radiat Prot Environ [serial online] 2016 [cited 2022 Aug 13];39:447. Available from: https://www.rpe.org.in/text.asp?2016/39/1/44/185182 
Introduction   
Dose to members of public due to atmospheric releases of radioactivity from nuclear power plants during normal and emergency period is evaluated as per regulatory requirement. The dose estimation involves atmospheric dispersion model to predict concentration distribution and computational methodology. ^{[1]} Atmospheric dispersion of the released air pollutants from various sources within the atmospheric planetary boundary layer is the result of combined effects of meteorological factors such as wind velocity, wind direction, temperature, and relative humidity together with earth surface topographic features. ^{[2]} The topographic characteristics of a complex terrain site are significantly different from those at flat terrain site due to different type of wind flow conditions. ^{[2]} The accurate knowledge of the surface topographic parameters, which represent terrain characteristics of a site, is required for many meteorological purposes including atmospheric dispersion modelling. In this regard, the surface roughness of a site is one of the important parameters which decides the wind flow. The rough surface retards the wind flow from a smooth surface, which results in sharp decrease in wind speed, change in vertical profile along with atmospheric stability. The roughness length (Z_{0} ) is used to quantify the surface roughness of an area or region in terms of surface elements configuration. Estimates of Z_{0} is necessary for examining the behavior of turbulence in urban areas, engineering applications involved with local wind structure, modeling of air pollution, representing the impact of urban areas on weather and climate in numerical models and vital to the study of urban microclimates. ^{[3]} Different methods are employed for the estimation of Z_{0} . In 1933, Nikaradse approximated the roughness length, Z_{0} = h/30 based on measurements of fluid flow in pipes uniformly roughened with grains of sand (where h is the average roughness element height of the area or region). ^{[3]} Lettau (1969) provided analytical methods to estimate Z_{0} based on the dimensions and spacing of the structures at a site. ^{[4]} Recently, semiempirical approach that incorporates both quantitative information from satellite image and qualitative information (land cover roughness) had been used to determine the Z_{0} . ^{[5]} Extensive studies on the wind profiles from measurements establishes expression of wind speed (U) variation with height (Z) and favored Z_{0} and U_{*} estimation from measured wind profile analysis. The estimation of site specific surface scaling parameters such as surface roughness length Z_{0} and friction velocity U_{*} , which are related to the roughness characteristics of the terrain require wind speed profile (0200 m height from surface) for the concerned site. In this paper, sector dependent Z_{0} and U_{*} for Kalpakkam site is evaluated under neutral conditions for the year 2014 using data measured in 50 m height meteorological tower at Kalpakkam. The sector dependent and nature of Z_{0} and U_{*} parameters in relation to the site topographic features and its consequence in the atmospheric dispersion models are discussed.
Materials and methods   
It is well established through the empirical data that in the homogeneous (noncomplex) terrain up to a height of about 200 m above ground level, the wind speed (U) increases with height (Z). ^{[6]} The function of wind speed U (Z) with height (Z) depends on height range from ground level, atmospheric stability class, and surface roughness length Z_{0} , which is defined as height Z_{0} at which wind speed U (Z) = 0. The surface roughness length Z_{0} is associated with the roughness characteristics of the terrain which predominantly influence the flow of wind. ^{[7]} The value of Z_{0} can be obtained through the observations of the wind speed profile under neutral atmospheric stability class conditions with the assumption of uniform distribution of obstacles, from the logarithmic wind profile relation:
where U is mean wind speed at height Z, U_{*} is friction velocity, and k is Von Karman constant. Friction velocity U_{*} is governed by the surface roughness of the terrain and atmospheric stability. ^{[6]} where τ is the shear stress and ρ is the density of air.
The 2014 year measured wind profile data were used for Z_{0} estimation. The wind profile data were observed from sensors installed at 10, 30, and 50 m levels in 50 m height meteorological tower located at Anupuram DAE Township, Kalpakkam. This meteorological tower is 4.3 km radial distance from Madras Atomic Power Station (MAPS), Kalpakkam. The hourly wind speed profiles were classified into unstable (A, B, and C), neutral (D), and stable (E and F) stability classes based on the standard deviation of the wind direction in combination with the scalar mean wind speed at 10 m height observations. ^{[6]} From the set of neutral wind profiles (2714 h in the year 2014), only those profiles (2370 h) were selected for which the concurrent wind direction profile shows insignificant variation with height (within ± 11.25°) ^{[2]} to the analysis. These wind profiles are further grouped into 16 sectors of wind direction compass and examined through linear curve fit investigation. The intercept (−log [Z _{0} ]) and slope (k/U_{*} ) of the best fitting straight line gives the value of Z_{0} and U_{*} , respectively, for each profile of the corresponding sector. [Table 1] shows the sector wise median Z_{0} values and associated standard deviation. [Table 2] shows the monthly sector wise median Z_{0} values.{Table 2}
Results and discussion   
Surface roughness length Z_{0} indicates the upwind sector terrain nonhomogeneity; it has been evaluated for different sectors of Kalpakkam complex terrain. [Figure 1] shows the interpolated roughness length for different sectors where median Z_{0} is in the center line of each sector with respect to wind profile observed location. The sectors from WNW to NNE with respect to meteorological tower, show lower values of Z_{0} about 0.3 m, may be due to sparse presence of buildings and predominance of agriculture farm land. The maximum and minimum Z _{0} are SSE (0.84 m) and N (0.26) upwind sectors, respectively [Table 1]. The SSE sector with respect to tower corresponds to S sector with respect to MAPS reflects relatively higher level of nonhomogeneities in the terrain due to the presence of more number of residential buildings at Sadras, Vengambakkam villages, high traffic, and sand dunes (≈15 m height) formed due to dumping of fast reactor fuel cycle and fuel reprocessing (FRFCF) large area excavation of soil, in contrast to N sector where mostly agricultural farming lands are present.  Figure 1: Surface roughness length Z0 for Kalpakkam site. *Madras Atomic Power Station, Kalpakkam; 50 m meteorological tower at Anupuram DAE township, Kalpakkam
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The monthly distribution of surface roughness length Z_{0} are estimated as it is seasonal dependent. From [Figure 2] and [Table 2], the highest value of surface roughness length Z_{0} for all sectors compared to its yearly median value given in [Table 1], was observed for May month. Highest values of surface roughness length Z_{0} was observed in SSE sector with respect to measurement location during April, May, and June months, which reflects urban terrain. The friction velocity U_{*} is a determinant of mechanical turbulence as turbulent eddies create fluctuations in velocity U, which results in shear τ. The higher amount of shear leads to stronger turbulence. [Table 3] shows the median friction velocity U_{*} for different sectors corresponding to neutral condition estimation. Surface drag coefficient C_{D} is used to characterize the resistance of the wind flow due to surface features at different sites is which is often used in atmospheric dispersion models. It is defined as where U_{10} is the mean wind speed at 10 m reference height. The estimated mean C_{D} value for all sectors, 0.16 implies urban environment at Kalpakkam site. The wind speed is one of the important determinants for the amount of pollutant dispersion in the atmosphere. Regulatory dispersion models require extrapolation of wind speed U_{Ref} at the measured height Z_{Ref} to the wind speed U_{Stack} at stack height Z_{Stack} based on power law wind profile:
where P is wind profile index.
The average wind profile index P of neutral stability class for all sectors was derived to be 0.2 based on site specific estimates of surface roughness length Z_{0} . Presently, P = 0.14 for neutral stability class is being used at Kalpakkam based on Bhabha Atomic Research Centre (BARC)1412 manual.
Conclusions   
Sitespecific surface roughness length Z_{0} and friction velocity U_{*} , which are significant meteorological input parameters for regulatory atmospheric dispersion models and mesoscale atmospheric dispersion models were evaluated for all upwind sectors and all months. The estimates of Z_{0} and U_{*} for Kalpakkam complex site, sector wise values would be more appropriate as no single value for Z_{0} and U_{*} can be assigned to all sectors. The S sector with respect to MAPS shows higher level of nonhomogeneities terrain of urban environment. The site specific sector averaged wind profile index P (0.2) was calculated based on surface roughness length Z_{0} for all sectors of neutral category. This can be used to accurate computation of annual dilution factor and annual gamma dose due to atmospheric releases of radioactivity at Kalpakkam site on the part of neutral category. The estimated mean surface drag coefficient C_{D} 0.16, implies urban environment at Kalpakkam site.
Acknowledgements
We would like to thank Dr. K.S. Pradeepkumar, Associate Director, Health, Safety and Environment Group, BARC, for giving encouragement in research activities. We thank Shri R. Satyanarayana, Station Director, and MAPS, for extending the requisite facilities and continuous support.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References   
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[Figure 1], [Figure 2]
[Table 1], [Table 3]
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