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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 2 | Copyright
Hydrol. Earth Syst. Sci., 22, 1371-1389, 2018
https://doi.org/10.5194/hess-22-1371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Feb 2018

Research article | 26 Feb 2018

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

Md Abul Ehsan Bhuiyan1, Efthymios I. Nikolopoulos1,2, Emmanouil N. Anagnostou1, Pere Quintana-Seguí3, and Anaïs Barella-Ortiz3,4 Md Abul Ehsan Bhuiyan et al.
  • 1Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA
  • 2Innovative Technologies Center S.A., Athens, Greece
  • 3Ebro Observatory, Ramon Llull University – CSIC, Roquetes (Tarragona), Spain
  • 4Castilla-La Mancha University, Toledo, Spain

Abstract. This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000–2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5km 1 h−1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20–99 and 44–88%, respectively, when considering the ensemble mean.

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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...
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