Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta

Tran, Anh Phuong; Son, Duong Hong; Duc, Nguyen Anh; Van Chien, Pham; Nguyen, Thanh Thuy; Tran, Manh Cuong; Nguyen, Nhat Anh; Le, Phong V. V.; Pham, Hai V.

doi:10.1007/s10661-023-11947-7

Title: Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta

Journal Article · 04 November 2023 · Environmental Monitoring and Assessment

DOI: https://doi.org/10.1007/s10661-023-11947-7 · OSTI ID:2224149

^[1]; Son, Duong Hong ^[1]; Duc, Nguyen Anh ^[1]; Van Chien, Pham ^[2]; Nguyen, Thanh Thuy ^[2]; Tran, Manh Cuong ^[1]; Nguyen, Nhat Anh ^[3];

^[4]; Pham, Hai V. ^[5]

Water Resources Inst., Hanoi (Vietnam)
Thuyloi Univ., Hanoi (Vietnam)
Technische Univ. of Dortmund (Germany)
Vietnam National Univ., Hanoi (Vietnam); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
INTERA Inc., Austin, TX (United States)

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. Here, this study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash–Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt²) is lower than that of Landsat estimation (10.4 ppt²) and numerical simulations (9.6 ppt²). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2224149

Journal Information:: Environmental Monitoring and Assessment, Vol. 195, Issue 12; ISSN 0167-6369

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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54 ENVIRONMENTAL SCIENCES
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