气候变化领域集成服务门户(CCPortal): A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula

CCPortal

DOI	10.5194/hess-22-1371-2018
	A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula
	Bhuiyan M.A.E.; Nikolopoulos E.I.; Anagnostou E.N.; Quintana-Seguí P.; Barella-Ortiz A.
发表日期	2018
ISSN	1027-5606
起始页码	1371
结束页码	1389
卷号	22 期号:2
英文摘要	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 5 km-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. © 2018 Author(s).
语种	英语
scopus关键词	Soil moisture; Stream flow; Atmospheric reanalysis; Distributed hydrological model; Global precipitation; Hydrological response; Land surface modeling; Satellite precipitation products; Statistical techniques; Streamflow simulations; Precipitation (meteorology); air temperature; data set; hydrological response; model validation; numerical model; precipitation intensity; satellite data; simulation; statistical analysis; streamflow; Iberian Peninsula
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160106
作者单位	Bhuiyan, M.A.E., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; Nikolopoulos, E.I., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States, Innovative Technologies Center S.A., Athens, Greece; Anagnostou, E.N., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; Quintana-Seguí, P., Ebro Observatory, Ramon Llull University-CSIC, Roquetes (Tarragona), Spain; Barella-Ortiz, A., Ebro Observatory, Ramon Llull University-CSIC, Roquetes (Tarragona), Spain, Castilla-La Mancha University, Toledo, Spain
推荐引用方式 GB/T 7714	Bhuiyan M.A.E.,Nikolopoulos E.I.,Anagnostou E.N.,et al. A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula[J],2018,22(2).
APA	Bhuiyan M.A.E.,Nikolopoulos E.I.,Anagnostou E.N.,Quintana-Seguí P.,&Barella-Ortiz A..(2018).A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula.Hydrology and Earth System Sciences,22(2).
MLA	Bhuiyan M.A.E.,et al."A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula".Hydrology and Earth System Sciences 22.2(2018).