scholarly journals A limited-memory acceleration strategy for MCMC sampling in hierarchical Bayesian calibration of hydrological models

2010 ◽  
Vol 46 (7) ◽  
Author(s):  
George Kuczera ◽  
Dmitri Kavetski ◽  
Benjamin Renard ◽  
Mark Thyer
Keyword(s):  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Bayesian Calibration ◽  
Limited Memory ◽  
Mcmc Sampling

scholarly journals Improving the efficiency of Monte Carlo Bayesian calibration of hydrologic models via model pre-emption

2015 ◽  
Vol 17 (5) ◽  
pp. 763-770 ◽  
Author(s):  
Mahyar Shafii ◽  
Bryan Tolson ◽  
L. Shawn Matott
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Hydrological Modelling ◽  
High Likelihood ◽  
Rainfall Runoff ◽  
Hydrologic Models ◽  
Bayesian Calibration ◽  
Computational Costs ◽  
Mcmc Sampling ◽  
Simulation Time

Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling and sequential Monte Carlo (SMC) sampling are popular methods for uncertainty analysis in hydrological modelling. However, application of these methodologies can incur significant computational costs. This study investigated using model pre-emption for improving the computational efficiency of MCMC and SMC samplers in the context of hydrological modelling. The proposed pre-emption strategy facilitates early termination of low-likelihood simulations and results in reduction of unnecessary simulation time steps. The proposed approach is incorporated into two samplers and applied to the calibration of three rainfall–runoff models. Results show that overall pre-emption savings range from 5 to 21%. Furthermore, results indicate that pre-emption savings are greatest during the pre-convergence ‘burn-in’ period (i.e., between 8 and 39%) and decrease as the algorithms converge towards high likelihood regions of parameter space. The observed savings are achieved with absolutely no change in the posterior set of parameters.

scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce the performance degradation. Many existing literatures model the time-varying parameters as functions of physically-based covariates; however, a major challenge remains finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes, and one scenario with a stationary scheme for model parameters, were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with stationary setting of model parameters; (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance; and (3) model parameters calibrated over dry periods were not suitable for predicting runoff over wet periods because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Vol 23 (8) ◽  
pp. 3405-3421 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

Failure Estimation of Offshore Renewable Energy Devices Based on Hierarchical Bayesian Approach

2019 ◽  
Author(s):  
Mohammad Mahdi Abaei ◽  
Nu Rhahida Arini ◽  
Philipp R. Thies ◽  
Johanning Lars
Keyword(s):  
Renewable Energy ◽  
Reliability Assessment ◽  
Tidal Energy ◽  
Bayesian Framework ◽  
Reliability Estimation ◽  
Observation Data ◽  
Hierarchical Bayesian ◽  
Energy Devices ◽  
Mcmc Sampling ◽  
Component Failures

Abstract Improving the reliability of marine renewable energy devices such as wave and tidal energy convertors is an important task, primarily to minimize the perceived risks and reduce the associated cost for operation and maintenance. Marine systems involve a wide range of uncertainties, due to the complexity of failure mechanism of the marine components, scarcity of data, human interactions and randomness of the sea environment. The fundamental element of a probabilistic risk analysis necessarily needs to rely on operational information and observation data to quantify the performance of the system. However, in reality it is difficult to ascertain observation of the precursor data according to the number of component failures that have occurred, mainly as a result of imprecision in the failure criterion, record keeping, or experimental and physical modelling of the process. Traditional reliability estimation approaches such as Fault Tree, Event Tree and Reliability Block Diagram analysis offer simplified, rarely realistic models of this complex reliability problem. The main reason is that they all rely on accurate prior information as a perquisite for performing reliability assessment. In this paper, a hierarchical Bayesian framework is developed for modelling marine renewable component failures encountered the uncertainty. The proposed approach is capable to incorporate the conditions, which lack reliable observation data (e.g. unknown/uncertain failure rate of a component). The hierarchical Bayesian framework provides a platform for the propagation of uncertainties through the reliability assessment of the system, via Markov Chain Monte Carlo (MCMC) sampling. The advantages of using MCMC sampling has proliferated Bayesian inference for conducting risk and reliability assessment of engineering system. It is able to use hyper-priors to represent prior parameters as a subjective observations for probability estimation of the failure events and enable an updating process for quantitative reasoning of interdependence between parameters. The developed framework will be an assistive tool for a better monitoring of the operation in terms of evaluating performance of marine renewable system under the risk of failure. The paper illustrates the approach using a tidal energy convertor as a case study for estimating components failure rates and representing the uncertainties of system reliability. The paper will be of interest to reliability practitioners and researchers, as well as tidal energy technology and project developers, seeking a more accurate reliability estimation framework.

Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE

2015 ◽  
Vol 316 ◽  
pp. 14-27 ◽  
Author(s):  
Xi Li ◽  
Jagadeesh Yeluripati ◽  
Edward O. Jones ◽  
Yoshitaka Uchida ◽  
Ryusuke Hatano
Keyword(s):  
Nitrous Oxide ◽  
Nitrogen Monoxide ◽  
Ecosystem Model ◽  
Hierarchical Bayesian ◽  
Bayesian Calibration
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