Development of a copula‐based particle filter (<scp>C</scp>op<scp>PF</scp>) approach for hydrologic data assimilation under consideration of parameter interdependence

Fan, Yurui; Huang, Guohe; Baetz, Brian W.; Li, Y. P.; Huang, Kai

doi:10.1002/2016wr020144

Cited by 53 publications

(32 citation statements)

References 71 publications

(131 reference statements)

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“…Furthermore, reliability over the project life is commonly chosen to describe the probability that the hydraulic structure will remain in a satisfactory state within its project life [60]. In the present study, the "AND" joint return period case is applied to define the bivariate risk and reliability through Equations (18)- (20). Here, the service time of a river levee is assumed to be 10 years, i.e., n = 10.…”

Section: Bivariate Return Period Risk and Reliability Analysis Basedmentioning

confidence: 99%

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Guo

Chang

Wang

et al. 2017

Entropy

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Copula functions have been extensively used to describe the joint behaviors of extreme hydrological events and to analyze hydrological risk. Advanced marginal distribution inference, for example, the maximum entropy theory, is particularly beneficial for improving the performance of the copulas. The goal of this paper, therefore, is twofold; first, to develop a coupled maximum entropy-copula method for hydrological risk analysis through deriving the bivariate return periods, risk, reliability and bivariate design events; and second, to reveal the impact of marginal distribution selection uncertainty and sampling uncertainty on bivariate design event identification. Particularly, the uncertainties involved in the second goal have not yet received significant consideration. The designed framework for hydrological risk analysis related to flood and extreme precipitation events is exemplarily applied in two catchments of the Loess plateau, China. Results show that (1) distribution derived by the maximum entropy principle outperforms the conventional distributions for the probabilistic modeling of flood and extreme precipitation events; (2) the bivariate return periods, risk, reliability and bivariate design events are able to be derived using the coupled entropy-copula method; (3) uncertainty analysis highlights the fact that appropriate performance of marginal distribution is closely related to bivariate design event identification. Most importantly, sampling uncertainty causes the confidence regions of bivariate design events with return periods of 30 years to be very large, overlapping with the values of flood and extreme precipitation, which have return periods of 10 and 50 years, respectively. The large confidence regions of bivariate design events greatly challenge its application in practical engineering design.

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Section: Bivariate Return Period Risk and Reliability Analysis Basedmentioning

confidence: 99%

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Guo

Chang

Wang

et al. 2017

Entropy

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“…The established model is then used to conduct bias correction for simulated precipitation in projected future periods. The detailed theoretical calculation process is as follows: once the copula function (mentioned above) is established, the joint probability density function f ( x 1 , x 2 , … , x n ) corresponding to the joint CDF F ( x 1 , x 2 , … , x n ) can be obtained by a product of the marginal densities and copula probability density c ( u 1 , u 2 , … , u n ) (Fan et al, ),

f ((),,, x_{1} x_{2} \dots x_{n}) = \frac{\partial^{n} C ((),,, u_{1} u_{2} \dots u_{n})}{\partial u_{1} \partial u_{2} \dots \partial u_{n}} \frac{\partial u_{1}}{\partial x_{1}} \frac{\partial u_{2}}{\partial x_{2}} \dots \frac{\partial u_{n}}{\partial x_{n}} = c ((),,, u_{1} u_{2} \dots u_{n}) \prod_{i = 1}^{n} f_{i} ((), x_{i}),

where f i ( x i ) is the probability density function of F i ( x i ).…”

Section: Methodsmentioning

confidence: 99%

Drought Occurring With Hot Extremes: Changes Under Future Climate Change on Loess Plateau, China

et al. 2019

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Drought is one of the most widespread and destructive hazards over the Loess Plateau (LP) of China. Due to climate change, extremely high temperature accompanied with drought (expressed as hot drought) may lead to intensive losses of both properties and human deaths in future. A hot drought probabilistic recognition system is developed to investigate how potential future climate changes will impact the simultaneous occurrence of drought and hot extremes (hot days exceeding certain values) on the LP. Two regional climate models, coupled with multiple bias‐correction techniques and multivariate probabilistic inference, are innovative integrated into the hot drought probabilistic recognition system to reveal the concurrence risk of droughts and hot extremes under different Representative Concentration Pathway (RCP) scenarios. The hot‐day index, TX90p, indicating the number of days with daily maximum temperature (Tmax) exceeding the 90th percentile threshold, and the Standardized Precipitation Index are applied to identify the joint risks on the LP using copula‐based methods. The results show that precipitation will increase throughout most of the LP under both RCP4.5 and RCP8.5 scenarios of 2036–2095, while Tmax may increase significantly all over the LP (1.8–2.7 °C for RCP4.5 and 2.7–3.6 °C for RCP8.5). The joint return periods of Standardized Precipitation Index and TX90p show that fewer stations will experience severe drought with long‐term hot extremes in two future scenarios. However, some stations may experience hot droughts that are more frequent and extreme, particularly certain stations in the southwest and south‐central regions of the LP with recurrence period less than 10 years.

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“…The main limitation of this study is that there is only one GCM is used, which is unable to deal with the uncertainties in future projections. However, a majority of uncertainties are associated with the choice of driving GCM, which will have a significant impact on the RCM-simulated precipitation and therefore eventually affect the projected changes in precipitation extremes (Déqué et al, 2007). Such uncertainties can be evaluated and quantified through an ensemble approach.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

“…As an efficient method, the ensemble approach is widely employed to explore the full range of multiple projections (Wang, Huang, Lin, et al, 2014), which would advance the understanding of the uncertainties in atmospheric and related processes. Therefore, it is preferable to provide a reliable climate projection with a higher resolution (i.e., 25 km) by dynamically downscaling multiple GCMs Déqué et al, 2007), which would deserve future research efforts.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

“…According to Environment and Climate Change Canada (), warming rates in Canada are about twice the global rate for centuries due to long‐lived greenhouse gases (GHGs) and warming oceans. Moreover, changes in precipitation extremes, floods, and droughts will have significant impacts on society, leading to environmental hazards and severe disruptions in economic activities (Fan et al, ; Mladjic et al, ; Shabbar & Bonsal, ). It is thus necessary to explore the impact of warmer climate on precipitation extremes over Canada and the inherent mechanism for RCPs.…”

Section: Introductionmentioning

confidence: 99%

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Future Changes in Precipitation Extremes Over Canada: Driving Factors and Inherent Mechanism

et al. 2018

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In this study, future changes in precipitation extremes over 10 climatic regions in Canada and their mechanism under Representative Concentration Pathways (RCPs) throughout the 21st century are investigated by using the Providing Regional Climates for Impacts Studies (PRECIS) model. The performance of PRECIS in hindcasting total and extreme precipitation for the historical period is first evaluated through two experiments driven by the boundary conditions from both ERA‐Interim (1979–2011) and HadGEM2‐ES (1959–2005). The validation results indicate that PRECIS can reasonably reproduce both the magnitudes and spatial patterns of precipitation extremes over Canada. Changes in total and extreme precipitation for two future periods are analyzed to explore how regional climate over different climatic regions would respond to global warming. Mechanism governing changes in precipitation extremes is explored through a comprehensive analysis of potential climate factors and their correlations and interactions with precipitation extremes. There are obvious increasing trends over most regions for the magnitude of precipitation extremes except for the duration indices. Averages of projected precipitation extremes over the climatic regions in Canada are projected to increase under RCP4.5. Such increases under RCP8.5 would be amplified due to higher greenhouse gas emissions. The projected changes in total precipitation are dominated by changes in wind velocity and relative humidity (e.g., changes in horizontal water vapor flux that would have significant effects on the occurrence of precipitation in Canada). In addition, the changes in the majority of precipitation extremes are commonly attributed to the changes in the saturation vapor pressure due to warmer temperature as described by the Clausius‐Clapeyron equation.

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Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

Cited by 53 publications

References 71 publications

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Drought Occurring With Hot Extremes: Changes Under Future Climate Change on Loess Plateau, China

Future Changes in Precipitation Extremes Over Canada: Driving Factors and Inherent Mechanism

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