Bibliographic citations
Asurza, F., (2017). Comparación de los productos TRMM [Tropical Rainfall Measuring Mission] y GPM [Global Precipitation Measurement] para el modelamiento hidrológico en la cuenca del río Huancané [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/3000
Asurza, F., Comparación de los productos TRMM [Tropical Rainfall Measuring Mission] y GPM [Global Precipitation Measurement] para el modelamiento hidrológico en la cuenca del río Huancané [Tesis]. : Universidad Nacional Agraria La Molina; 2017. https://hdl.handle.net/20.500.12996/3000
@misc{renati/248359,
title = "Comparación de los productos TRMM [Tropical Rainfall Measuring Mission] y GPM [Global Precipitation Measurement] para el modelamiento hidrológico en la cuenca del río Huancané",
author = "Asurza Véliz, Flavio Alexander",
publisher = "Universidad Nacional Agraria La Molina",
year = "2017"
}
The current research has the main purpose to assess the capacity of gridded precipitation products of Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) in its application to hydrologic modelling. A quantitative comparison was made among these satellite products with a low dense gauge network over Huancané basin in Peruvian Altiplano. In general, comparisons of 3B42RT, 3B42V7 and IMERG estimates with gauge observations over a period from April 2014 to December 2015 at daily resolution show that IMERG estimates correctly precipitation patterns (R2: 0.38), however it doesn’t quantify properly the rain intensity (BIAS: -32%). On the other hand, the product 3B42V7 captures very well the rain intensity in contrast to the 3B42RT that show high overestimation. An initial hydrologic assessment with GR4J lumped hydrologic model using satellite products without correction indicate that, in lack of gauge observations, 3B42V7 is the best option to simulate daily streamflow. Finally, when performing the mean field bias correction to IMERG, results show that the precipitation estimation is improved (R2: 0.9; BIAS: -10.1%), consequently the IMERG corrected is the best precipitation satellite product to its application in hydrologic modelling. These preliminary results are limited to the short-term analysis, however as more IMERG data is released, more studies to explore its usefulness in water and climate change aspects will be needed.
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