globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-4633-2018
论文题名:
A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations
作者: Pugliese A.; Persiano S.; Bagli S.; Mazzoli P.; Parajka J.; Arheimer B.; Capell R.; Montanari A.; Blöschl G.; Castellarin A.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:9
起始页码: 4633
结束页码: 4648
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
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163206
Appears in Collections:气候变化与战略

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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

Recommended Citation:
Pugliese A.,Persiano S.,Bagli S.,et al. A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations[J]. Hydrology and Earth System Sciences,2018-01-01,22(9)
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