article · Water · 2018
Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes
Rajarshi Das Bhowmik, Seung Beom Seo, Saswata Sahoo
Cited by 4Open access
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.
Cite this paper
Das Bhowmik, R., Seo, S. B., & Sahoo, S. (2018). Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes. Water, 10(7), 875. https://doi.org/10.3390/w10070875
Das Bhowmik, Rajarshi, et al. “Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes.” Water, vol. 10, no. 7, 2018, p. 875, https://doi.org/10.3390/w10070875.
Das Bhowmik, Rajarshi, Seung Beom Seo, and Saswata Sahoo. 2018. “Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes.” Water 10 (7): 875. https://doi.org/10.3390/w10070875.
Das Bhowmik, R., Seo, S.B. and Sahoo, S. (2018) ‘Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes’, Water, 10(7), p. 875. Available at: https://doi.org/10.3390/w10070875.
@article{dasbhowmik2018streamflow,
title = {{Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes}},
author = {Das Bhowmik, Rajarshi and Seo, Seung Beom and Sahoo, Saswata},
journal = {Water},
volume = {10},
number = {7},
pages = {875},
year = {2018},
doi = {10.3390/w10070875}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.