DOI: 10.5194/hess-24-3967-2020
论文题名: Stochastic simulation of streamflow and spatial extremes: A continuous; wavelet-based approach
作者: I. Brunner M. ; Gilleland E.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 8 起始页码: 3967
结束页码: 3982
语种: 英语
Scopus关键词: Catchments
; Cell proliferation
; Frequency domain analysis
; Random processes
; Risk perception
; Stochastic models
; Stream flow
; Time series
; Water management
; Wavelet transforms
; Continuous simulation
; Estimation applications
; Inverse wavelet transforms
; Management applications
; Spatio-temporal models
; Stochastic simulations
; Stochastic time series
; Wavelet-based approach
; Stochastic systems
; extreme event
; simulation
; stochasticity
; streamflow
; wavelet analysis
英文摘要: Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but as yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets (PRSim.wave) which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of the kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States.We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional ater management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential. Highlights. 1. Stochastic simulation of continuous streamflow time series using an empirical, wavelet-based, spatio-temporal model in combination with the parametric kappa distribution. 2. Generation of stochastic time series at multiple sites showing temporal short- and long-range dependence, on-stationarities, and spatial dependence in extreme events. 3. Implementation of PRSim.wave in R package PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162620
Appears in Collections: 气候变化与战略
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作者单位: I. Brunner, M., Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, United States; Gilleland, E., Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, United States
Recommended Citation:
I. Brunner M.,Gilleland E.. Stochastic simulation of streamflow and spatial extremes: A continuous; wavelet-based approach[J]. Hydrology and Earth System Sciences,2020-01-01,24(8)