Inversion Research on Aerodynamic Roughness of Different Underlying Surfaces in Aral Sea Basin

. The study of water and heat flux in Aral Sea Basin is a hot topic in the study of water resources in Central Asia. Aerodynamic roughness is the key parameter of inversion. is retrieved based on NDVI and Albedo simulation and verified by the vegetation height in Central Asia. The results show that the inversion of by this model is basically reasonable. In 2018, the monthly average value of is about 0.056m, the vegetation height in Aral Sea Basin is about 0.44m, and both and vegetation height in space are higher in eastern mountainous areas than in western desert areas.


Introduction
Aerodynamic Roughness ( , Aerodynamic Roughness Length) refers to the fact that when airflow passes through a certain physical boundary, if the flow velocity near the boundary is zero, then the physical boundary is said to be aerodynamically rough, and its size mainly depends on the surface roughness, that is, the distribution of underlying surface types [1] .
is a key parameter for studying surface water heat flux, and is also a basic parameter for estimating thermodynamic roughness ( Thermal Roughness Length), additional damping of heat transfer (KB (-1) ), regional sensible heat flux and latent heat flux. Aral Sea Basin is located in the center of Eurasia. It is a typical arid and semi-arid region with a general lack of water resources [2,3] . The surface type in this region is complex. The accurate estimation and verification analysis of can provide a more accurate parameter basis for regional energy balance and remote sensing simulation of surface energy in this region.

Overview of Research Area
The Aral Sea Basin covers a total area of 1.76 million km 2 , involving five Central Asian countries and Afghanistan and Iran [2,4] . Its geographical division is shown in Figure 1. Iranian mountainous areas. Aral Sea Basin is a continental climate with typical temperate desert and grassland, with sufficient sunshine and little rain (Han Qifei, 2018; Chen Qichuan, 2012), the annual maximum rainfall is 200mm, and the annual rainfall in consecutive dry years is 65mm, and the spatial distribution is uneven. The rainfall in Turan desert area in the southwest of the basin is less than 100 mm. Based on the analysis of the typical vegetation height of each division based on the vegetation division, as shown in Figure 2 and Table 1, the study area is widely distributed in the Haloxylon ammodendron desert area (702), mainly in the Turan desert area. Its typical trees are Haloxylon ammodendron 1-7m, shrubs are Calligonum Bai Pu 20-150cm, and typical vegetation in grassland, Gobi and desert is Salix psammophila 14-60 cm. The second is savanna type grass (1012), and the typical vegetation is bulbous barley 70-90 cm; In the desert region of Central Kazakhstan, the vegetation is divided into the central desert Tugailin and meadow (802), with the representative trees being Elaeagnus angustifolia 5-10m, shrubs being Tamarix ramosissima 3-6m, and herbaceous vegetation being Agropyron strobilacea 80cm; In addition, there is also Artemisia desert region (705) in the desert region of central Kazakhstan, where shrubs are 8-30cm in diameter and herbs are 25-35cm in diameter.

and H Inversion Model
Currently, is estimated in many ways and mostly by empirical formulas. This paper is based on the empirical formula proposed by Liu [5,7] , which can estimate and H in a large range using remote sensing data as shown in formula (1) (2). The remote sensing image is the 30-day composite daily mean data of MODIS NDVI and albedo1km in 2018. In the simulation process, using the geographical division and vegetation type division of Central Asia [6] and the land cover of Aral Sea basin, the typical representative vegetation maximum height values of each vegetation division in the study area (as shown in Table 1) are obtained and spatially analyzed. It is proposed to compare the simulated values of H and June, analyzed the applicability and rationality of the empirical formula in Aral Sea region, and revise the inversion results. The vegetation code is shown in Figure 2. (1) Where, a and b are empirical coefficients, which refers to 0.0553 and 3.64 respectively; α is broad band albedo and NDVI is normalized difference vegetation index.

Rationality Analysis of Simulation Results
The simulated vegetation height in June based on inversion is compared with the spatial distribution map of vegetation height formed by typical vegetation gridding based on vegetation zoning and surface classification, as shown in in the region, with the exception of savannah shrub sparse forest and grass and mountain cold xerophytic subshrub grassland. The average height of simulated vegetation is 1.86m and 1.31m respectively, and the height of typical vegetation is 1.08m and 0.92m respectively. By comparing the simulated vegetation height and typical vegetation height of different land types, different geographical divisions and vegetation divisions, the simulated values are generally reasonable.

The analysis of simulation result
The analysis of the simulation result is based on the change of the monthly mean value of 2018 in different geographical areas as shown in Figure 3. According to the value, it is divided into 5 grades. The corresponding land types of 0.002~0.0029m include permanent glacier and snow, beach, saline-alkali land, wetland and desert. The maximum of wetland and desert reaches the annual peak value of 0.0028m around April, and the of other land types is between 0.0025~0.0028m; The corresponding land types of 0.006~0.013m are dry land, lake, Gobi and bare land, among which in dry land rose from 0.0065m to 0.0075 m from March to October. The changes of in bare land, Gobi and lake are not big. 0.15~0.45m, bush forest, urban land and rural residential area are 0.20m, 0.40m and 0.30m respectively; The of paddy field, irrigated land, high coverage grassland, medium coverage grassland, low coverage grassland, canals, reservoir ponds and bare rock gravelly land fluctuates between 0.018 m and 0.080 m. The value of forested land fluctuates between 1.86~2.0m, with the highest month being around June.

Conclusion
The results of dynamic roughness of Aral sea basin based on NDVI and Albedo simulation inversion are reasonable. In 2018, the monthly average value of z_om is about 0.056m, the eastern part is larger than the western part in space, the mountainous area is higher than the plain desert, and the woodland, grassland and town land are larger than other land types, especially the Gobi and desert areas, z_om is about 0.003m m.