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Water 2018, 10(7), 875; https://doi.org/10.3390/w10070875

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

1
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
2
Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
3
Walmart Labs, Salarpuria Touchstone, Outer Ring Rd, Bengaluru, Karnataka 560103, India
*
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 23 June 2018 / Accepted: 28 June 2018 / Published: 30 June 2018
(This article belongs to the Section Hydrology)
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Abstract

Statistical models for hydrologic simulation are a common choice among researchers particularly when catchment information is limited. In this study, we adopt a new statistical approach, namely Bayesian regression with multivariate linear spline (BMLS) for long-term simulation of streamflow on a Hydroclimate Data Network (HCDN) site in the United States. The study aims to: (i) evaluate the performance of the BMLS model; (ii) compare the performance of climate model outputs as predictors in hydrologic simulation; and (iii) estimate the changes in streamflow caused by anthropogenic climate change which is defined as the projected change in precipitation and temperature under different greenhouse gas emission scenarios. Performance of the BMLS model is compared with climatology for the validation period. Results suggest that the BMLS model forced with observed monthly precipitation and average temperature exhibits information that is not presented in the climatology of the validation period. Later, we consider Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and hindcast runs to simulate streamflow at the HCDN site. The study found that sea-surface temperature-initialized decadal hindcast runs are performing no better than 20th century historical runs regarding hydrologic simulation. Finally, the changes in mean and variability in streamflow at the HCDN site are estimated by forcing the model with CMIP5 future projections for the period 2000–2049. View Full-Text
Keywords: Bayesian; streamflow simulation; Bayesian regression with multivariate linear spline (BMLS); Coupled Model Intercomparison Project Phase 5 (CMIP5); hindcast Bayesian; streamflow simulation; Bayesian regression with multivariate linear spline (BMLS); Coupled Model Intercomparison Project Phase 5 (CMIP5); hindcast
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Das Bhowmik, R.; Seo, S.B.; Sahoo, S. Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes. Water 2018, 10, 875.

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