Journal
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
Volume 63, Issue 7, Pages 1020-1046Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2018.1469757
Keywords
Monsoon streamflow; machine learning; Bayesian analysis; climate change; Wainganga; India
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In this study, classification- and regression-based statistical downscaling is used to project the monthly monsoon streamflow over the Wainganga basin, India, using 40 global climate model (GCM) outputs and four representative concentration pathways (RCP) scenarios. Support vector machine (SVM) and relevance vector machine (RVM) are considered to perform downscaling. The RVM outperforms SVM and is used to simulate future projections of monsoon flows for different periods. In addition, variability in water availability with uncertainty and change point (CP) detection are accomplished by flow-duration curve and Bayesian analysis, respectively. It is observed from the results that the upper extremes of monsoon flows are highly sensitive to increases in temperature and show a continuous decreasing trend. Medium and low flows are increasing in future projections for all the scenarios, and high uncertainty is noticed in the case of low flows. An early CP is detected in the case of high emissions scenarios.
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