Uncertainty and bias in satellite-based precipitation estimates over Indian sub-continental basins: implications for real-time streamflow simulation and flood prediction

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dc.contributor.author Shah, Harsh L.
dc.contributor.author Mishra, Vimal
dc.date.accessioned 2015-12-02T10:39:44Z
dc.date.available 2015-12-02T10:39:44Z
dc.date.issued 2016-02
dc.identifier.citation Shah, Harsh L. and Mishra, Vimal, "Uncertainty and bias in satellite-based precipitation estimates over Indian sub-continental basins: implications for real-time streamflow simulation and flood prediction", Journal of Hydrometeorology, DOI: 10.1175/JHM-D-15-0115.1, vol. 17, no. 2, pp. 615-636, Feb. 2016.
dc.identifier.issn 1525-755X
dc.identifier.issn 1525-7541
dc.identifier.uri http://dx.doi.org/10.1175/JHM-D-15-0115.1
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/1993
dc.description.abstract Real-time streamflow monitoring is essential over the Indian sub-continental river basins as a large population is affected by floods. Moreover, streamflow monitoring helps in managing water resources in the agriculture dominated region. In this study, we systematically investigated the bias and uncertainty in satellite-based precipitation products [Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), Tropical rainfall Measurement Mission Real Time (TRMM-3B42RTV7), and gauge adjusted TRMM (TRMM-3B42V7)] over the Indian sub-continental river basins for the period of 2000-2013. Moreover, we evaluated the influence of bias in the satellite precipitation on real-time streamflow monitoring and flood assessment over the Mahanadi river basin. Results showed that the CMOPRH and PERSIANN underestimated daily mean precipitation over the majority of the sub-continental river basins. On the other hand, real-time product of the TRMM (TRMM-3B42RTV7) overestimated daily mean precipitation over most of the river basins in the sub-continent. While gauge adjusted products of the PERSIANN (PERSIANN-CDR) and TRMM (TRMM-3B42V7) performed better than their real-time products, large biases remain in their performance to capture extreme precipitation (both frequency and magnitudes) over the sub-continental basins. Among the real-time precipitation products, the TRMM-3B42RTV7 performed better than the other two (CMORPH and PERSIANN) over the majority of the Indian sub-continental basins. Daily streamflow simulations using the Variable Infiltration Capacity (VIC) model for the Mahanadi River basin showed a better performance by the TRMM-3B42RTV7 product than the other real-time datasets. Moreover, daily streamflow simulations over the Mahanadi River basin showed that bias in real-time precipitation products affect the initial condition and precipitation forcing, which in-turn affect flood peak timing and magnitudes. en_US
dc.description.statementofresponsibility by Harsh L. Shah and Vimal Mishra
dc.format.extent vol. 17, no. 2, pp. 615-636
dc.language.iso en_US en_US
dc.publisher American Meteorological Society en_US
dc.subject Real-time Streamflow Simulation en_US
dc.subject Satellite-based Precipitation en_US
dc.subject Morphing technique en_US
dc.subject Mahanadi River basin en_US
dc.title Uncertainty and bias in satellite-based precipitation estimates over Indian sub-continental basins: implications for real-time streamflow simulation and flood prediction en_US
dc.type Article en_US
dc.relation.journal Journal of Hydrometeorology


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