DOI: 10.5194/hess-23-2863-2019
论文题名: Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network
作者: Sungmin O. ; Foelsche U.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期: 7 起始页码: 2863
结束页码: 2875
语种: 英语
Scopus关键词: Catchments
; Errors
; Mean square error
; Rain gages
; Remote sensing
; Runoff
; Topography
; Uncertainty analysis
; High temporal resolution
; Horizontal grid spacing
; Horizontal resolution
; Rainfall measurements
; Rainfall uncertainties
; Root mean square errors
; Spatial variability
; Uncertainty informations
; Rain
; assessment method
; catchment
; network analysis
; power law
; rainfall
; remote sensing
; spatial analysis
; spatial variation
; uncertainty analysis
英文摘要: Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20km HYDRO 15km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10% of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (HYDRO % normalized root-mean-square error-RMSE-is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5min extreme intensity increases by 44% for the 99.9th and by 25% for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01 HYDRO. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study. © 2019 Copernicus GmbH. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162941
Appears in Collections: 气候变化与战略
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作者单位: Sungmin, O., Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics (IGAM/IP), NAWI Graz, University of Graz, Graz, Austria, FWF-DK Climate Change, University of Graz, Austria, Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany; Foelsche, U., Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics (IGAM/IP), NAWI Graz, University of Graz, Graz, Austria, FWF-DK Climate Change, University of Graz, Austria, Wegener Center for Climate and Global Change (WEGC), University of Graz, Graz, Austria
Recommended Citation:
Sungmin O.,Foelsche U.. Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network[J]. Hydrology and Earth System Sciences,2019-01-01,23(7)