气候变化领域集成服务门户(CCPortal): A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations

CCPortal

DOI	10.5194/hess-22-4633-2018
	A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations
	Pugliese A.; Persiano S.; Bagli S.; Mazzoli P.; Parajka J.; Arheimer B.; Capell R.; Montanari A.; Blöschl G.; Castellarin A.
发表日期	2018
ISSN	1027-5606
起始页码	4633
结束页码	4648
卷号	22 期号:9
英文摘要	Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980-2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2). © Author(s) 2018.
语种	英语
scopus关键词	Catchments; Rain; Stream flow; Cross validation; Flow duration curve; Geostatistical data; Geostatistical techniques; Macroscale models; Network density; Rainfall-runoff simulations; Ungauged basins; Runoff; calibration; data assimilation; geostatistics; rainfall-runoff modeling; simulation; streamflow; time series; Austria; Italy; Tyrol
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/159923
作者单位	Pugliese, A., Department DICAM, University of Bologna, Bologna, Italy; Persiano, S., Department DICAM, University of Bologna, Bologna, Italy; Bagli, S., GECOsistema srl, Cesena, Italy; Mazzoli, P., GECOsistema srl, Cesena, Italy; Parajka, J., Institute for Hydraulic and Water Resources Engineering, TU Wien, Vienna, Austria; Arheimer, B., Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden; Capell, R., Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden; Montanari, A., Department DICAM, University of Bologna, Bologna, Italy; Blöschl, G., Institute for Hydraulic and Water Resources Engineering, TU Wien, Vienna, Austria; Castellarin, A., Department DICAM, University of Bologna, Bologna, Italy
推荐引用方式 GB/T 7714	Pugliese A.,Persiano S.,Bagli S.,et al. A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations[J],2018,22(9).
APA	Pugliese A..,Persiano S..,Bagli S..,Mazzoli P..,Parajka J..,...&Castellarin A..(2018).A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations.Hydrology and Earth System Sciences,22(9).
MLA	Pugliese A.,et al."A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations".Hydrology and Earth System Sciences 22.9(2018).