Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

Water

Huihua Ruan¹  Kaiwen Zhong²  Jianhui Xu²  Xulong Liu²  Yi Zhao² 

[1] Guangdong Meteorological Observation Data Center, Guangzhou 510080, China;Guangzhou Institute of Geography, Guangzhou 510070, China;

关键词: composite curve number;    linear spectral mixture analysis;    normalized difference built-up index;    normalized difference vegetation index;    runoff;   

DOI  :  10.3390/w8120550

来源: DOAJ

【 摘 要 】

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

【 授权许可】

Unknown