Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019465 |
Bayesian spectral likelihood for hydrological parameter inference | |
Schaefli, Bettina1; Kavetski, Dmitri2 | |
2017-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:8 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; Australia |
英文摘要 | This paper proposes a spectral domain likelihood function for the Bayesian estimation of hydrological model parameters from a time series of model residuals. The spectral domain error model is based on the power-density spectrum (PDS) of the stochastic process assumed to describe residual errors. The Bayesian spectral domain likelihood (BSL) is mathematically equivalent to the corresponding Bayesian time domain likelihood (BTL) and yields the same inference when all residual error assumptions are satisfied (and all residual error parameters are inferred). However, the BSL likelihood function does not depend on the residual error distribution in the original time domain, which offers a theoretical advantage in terms of robustness for hydrological parameter inference. The theoretical properties of BSL are demonstrated and compared to BTL and a previously proposed spectral likelihood by Montanari and Toth (2007), using a set of synthetic case studies and a real case study based on the Leaf River catchment in the U.S. The empirical analyses confirm the theoretical properties of BSL when applied to heteroscedastic and autocorrelated error models (where heteroscedasticity is represented using the log-transformation and autocorrelation is represented using an AR(1) process). Unlike MTL, the use of BSL did not introduce additional parametric uncertainty compared to BTL. Future work will explore the application of BSL to challenging modeling scenarios in arid catchments and indirect calibration with nonconcomitant input/output time series. |
英文关键词 | hydrology Bayesian inference spectral domain inference rainfall-runoff modeling frequency domain inference model calibration |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000411202000029 |
WOS关键词 | CATCHMENT MODELS ; RUNOFF MODELS ; TIME-SERIES ; UNCERTAINTY ; CALIBRATION ; AUTOCORRELATION ; JOINT |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21217 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Lausanne, Inst Earth Surface Dynam, Fac Geosci & Environm, Lausanne, Switzerland; 2.Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA, Australia |
推荐引用方式 GB/T 7714 | Schaefli, Bettina,Kavetski, Dmitri. Bayesian spectral likelihood for hydrological parameter inference[J]. WATER RESOURCES RESEARCH,2017,53(8). |
APA | Schaefli, Bettina,&Kavetski, Dmitri.(2017).Bayesian spectral likelihood for hydrological parameter inference.WATER RESOURCES RESEARCH,53(8). |
MLA | Schaefli, Bettina,et al."Bayesian spectral likelihood for hydrological parameter inference".WATER RESOURCES RESEARCH 53.8(2017). |
条目包含的文件 | 条目无相关文件。 |
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