Application of Multivariate Observations in Hydrological Data Assimilation

YANG Chu-hui,LIN Lin,ZHANG Qiu-ru,SHI Liang-sheng

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China Rural Water and Hydropower ›› 2017 ›› (9) : 52-56.

Application of Multivariate Observations in Hydrological Data Assimilation

  • YANG Chu-hui,LIN Lin,ZHANG Qiu-ru,SHI Liang-sheng
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Abstract

Aims to investigate how observation datasets affect performance of data assimilation. The Ensemble Kalman Filter ( EnKF) is coupled with the distributed hydrologic model Soil Water Assessment Tool ( SWAT) to assimilate observations including streamflow,soil moisture,and water table. Results shows are as follows: when adding downstream streamflow separately,predictions of streamflow and CN2 are accurate,but soil moisture estimates of deep layers are biased. Adding surface soil moisture observation could predict storm runoff,but distort vertical moisture movement and base flow. Complementary groundwater information could reflect vertical movement of soil moisture, and improve estimates of soil moisture. Therefore,multivariate observations lead to more accurate model states,and low-cost and available groundwater observation is moderately useful when assimilating soil water.

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YANG Chu-hui,LIN Lin,ZHANG Qiu-ru,SHI Liang-sheng. Application of Multivariate Observations in Hydrological Data Assimilation. China Rural Water and Hydropower. 2017, 0(9): 52-56

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