A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins
Study region: The transboundary Imjin River basin, Korea. Study focus: The primary aim is to propose and validate a novel framework for assessing the uncertainty in hydrological models, particularly rainfall–runoff models (RRMs), considering transboundary river basins with limited data accessibility...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004440 |
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| author | Thi-Duyen Nguyen Duc Hai Nguyen Hyun-Han Kwon Deg-Hyo Bae |
| author_facet | Thi-Duyen Nguyen Duc Hai Nguyen Hyun-Han Kwon Deg-Hyo Bae |
| author_sort | Thi-Duyen Nguyen |
| collection | DOAJ |
| description | Study region: The transboundary Imjin River basin, Korea. Study focus: The primary aim is to propose and validate a novel framework for assessing the uncertainty in hydrological models, particularly rainfall–runoff models (RRMs), considering transboundary river basins with limited data accessibility. By utilizing an adaptive Markov chain Monte Carlo (MCMC) simulation method combined with three comprehensive uncertainty assessment measures, the developed framework focuses on evaluating the uncertainty inherent in RRMs. A key component of this framework is the delayed rejection adaptive Metropolis (DRAM) algorithm, which is employed to explore behavioral simulations defined by four likelihood functions (LFs). The proposed methodology was applied to the transboundary Imjin River basin using the Sejong University rainfall–runoff (SURR) model, a case study that involves a database of five-year extreme flood events. New hydrological insights for the region: The application of this framework in the transboundary Imjin basin demonstrated its effectiveness in quantifying and addressing the uncertainty in RRM predictions. The integration of the DRAM algorithm with uncertainty indices provided a robust mechanism for evaluating and improving the reliability of RRM outputs for transboundary basins. Effects of LFs in blending with the DRAM algorithm were confirmed by uncertainty measures and the behavior of the upper and lower uncertainty bounds. These insights could provide an approach to develop more accurate and reliable water resource management strategies in global transboundary contexts. |
| format | Article |
| id | doaj-art-3e2e7a97354c4c19b53280d696e8088a |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-3e2e7a97354c4c19b53280d696e8088a2025-01-22T05:42:00ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102095A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basinsThi-Duyen Nguyen0Duc Hai Nguyen1Hyun-Han Kwon2Deg-Hyo Bae3Department of Civil & Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 143-747, South Korea; Department of Civil Engineering, Vinh University, Vinh, 461010, VietnamDepartment of Civil, Geological and Environmental Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada; Faculty of Water Resources Engineering, Thuyloi University, 175 Tay Son Street, Dong Da District, Ha Noi 116705, Viet NamDepartment of Civil & Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 143-747, South KoreaDepartment of Civil & Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 143-747, South Korea; Corresponding author.Study region: The transboundary Imjin River basin, Korea. Study focus: The primary aim is to propose and validate a novel framework for assessing the uncertainty in hydrological models, particularly rainfall–runoff models (RRMs), considering transboundary river basins with limited data accessibility. By utilizing an adaptive Markov chain Monte Carlo (MCMC) simulation method combined with three comprehensive uncertainty assessment measures, the developed framework focuses on evaluating the uncertainty inherent in RRMs. A key component of this framework is the delayed rejection adaptive Metropolis (DRAM) algorithm, which is employed to explore behavioral simulations defined by four likelihood functions (LFs). The proposed methodology was applied to the transboundary Imjin River basin using the Sejong University rainfall–runoff (SURR) model, a case study that involves a database of five-year extreme flood events. New hydrological insights for the region: The application of this framework in the transboundary Imjin basin demonstrated its effectiveness in quantifying and addressing the uncertainty in RRM predictions. The integration of the DRAM algorithm with uncertainty indices provided a robust mechanism for evaluating and improving the reliability of RRM outputs for transboundary basins. Effects of LFs in blending with the DRAM algorithm were confirmed by uncertainty measures and the behavior of the upper and lower uncertainty bounds. These insights could provide an approach to develop more accurate and reliable water resource management strategies in global transboundary contexts.http://www.sciencedirect.com/science/article/pii/S2214581824004440Uncertainty qualificationHydrological modelDelayed rejection adaptive Metropolis algorithmAssessment indicesLikelihood functionTransboundary river basin |
| spellingShingle | Thi-Duyen Nguyen Duc Hai Nguyen Hyun-Han Kwon Deg-Hyo Bae A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins Journal of Hydrology: Regional Studies Uncertainty qualification Hydrological model Delayed rejection adaptive Metropolis algorithm Assessment indices Likelihood function Transboundary river basin |
| title | A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins |
| title_full | A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins |
| title_fullStr | A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins |
| title_full_unstemmed | A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins |
| title_short | A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins |
| title_sort | novel framework for uncertainty quantification of rainfall runoff models based on a bayesian approach focused on transboundary river basins |
| topic | Uncertainty qualification Hydrological model Delayed rejection adaptive Metropolis algorithm Assessment indices Likelihood function Transboundary river basin |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004440 |
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