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Proceedings Paper

Estimation of water budget by remote sensing in Taihu Basin, China
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Paper Abstract

Taihu Basin is located in the lower reach of the Yangtze River basin. Recent years, severe pollution in Lake Taihu was frequently occurred, which need to evaluate overall water balance of the basin. Evapotranspiration (ET) and precipitation are the key elements in water balance estimation that give scientifically sound information on water availability. Currently, satellite remote sensing is widely used for estimation of these two parameters. In this study, precipitation and ET from remote sensing and observing runoff are used to estimate annual variations in the water budget of the Taihu Lake Basin from 2005 to 2007. The Global Satellite Mapping of Precipitation (GSMaP) data was applied to estimate precipitation in spatial and temporal variability of the Taihu Basin. The surface temperature-normalized difference vegetation index (Ts-NDVI) triangle method with topographic correction was used to estimate ET from MODIS datasets in this study. Runoff was observed from hydrological station. The ET is the largest consumption in water budget components over Taihu Basin. For the whole basin, the ratio of ET/Rainfall is about 0.85-0.95 from 2005 to 2007, and it is about 1.2-1.4 for Lake Taihu. In general, the income terms of water balance in the basin including precipitation and inflow from Yangtze River should be equal to outgo terms including ET, outflow and water storage. But the income terms is mostly larger than outgo terms in Taihu Basin, the imbalance percentage is about 0.4-9.6% for the whole basin, and 0.5-3.7% for Lake Taihu.

Paper Details

Date Published: 8 October 2011
PDF: 8 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81742E (8 October 2011); doi: 10.1117/12.898000
Show Author Affiliations
Xiaosong Zhao, Nanjing Institute of Geography and Limnology (China)
Yuanbo Liu, Nanjing Institute of Geography and Limnology (China)


Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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