Articles | Volume 22, issue 2
https://doi.org/10.5194/hess-22-1371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-22-1371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula
Md Abul Ehsan Bhuiyan
Department of Civil and Environmental Engineering,
University of Connecticut, Storrs, CT, USA
Efthymios I. Nikolopoulos
Department of Civil and Environmental Engineering,
University of Connecticut, Storrs, CT, USA
Innovative Technologies Center S.A., Athens, Greece
Emmanouil N. Anagnostou
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering,
University of Connecticut, Storrs, CT, USA
Pere Quintana-Seguí
Ebro Observatory, Ramon Llull University – CSIC, Roquetes
(Tarragona), Spain
Anaïs Barella-Ortiz
Ebro Observatory, Ramon Llull University – CSIC, Roquetes
(Tarragona), Spain
Castilla-La Mancha University, Toledo, Spain
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Latest update: 22 Nov 2024
Short summary
This study investigates the use of a nonparametric model for combining multiple global precipitation datasets and characterizing estimation uncertainty. Inputs to the model included three satellite precipitation products, an atmospheric reanalysis precipitation dataset, satellite-derived near-surface daily soil moisture data, and terrain elevation. We evaluated the technique based on high-resolution reference precipitation data and further used generated ensembles to force a hydrological model.
This study investigates the use of a nonparametric model for combining multiple global...