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Title: Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers

Abstract

The Monsoon Asia region is home to ten of the world’s biggest rivers, supporting the lives of 1.7 billion people who rely on streamflow for water, energy, and food. Yet, a synoptic understanding of multi-centennial streamflow variability for this region is lacking. Here we produce the first large scale streamflow reconstruction over Monsoon Asia (63 stations in 16 countries), using a novel climate-informed dynamic algorithm that is skillful over 92% of the gauging stations. In this work, we show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans that exert their greatest influence over the Mekong and Chao Phraya basins. This work increases our understanding of streamflow variability over Monsoon Asia. We suggest that future water management in the region should be coordinated between basins, taking into account the states of the oceans.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [1]
  1. Singapore University of Technology and Design (Singapore)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Columbia Univ., New York, NY (United States)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1781090
Report Number(s):
PNNL-SA-148408
Journal ID: ISSN 0043-1397
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 56; Journal Issue: 12; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; streamflow reconstruction; Monsoon Asia; large sample hydrology; rivers; climate variability; teleconnection

Citation Formats

Nguyen, Hung T. T., Turner, Sean W. D., Buckley, Brendan M., and Galelli, Stefano. Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers. United States: N. p., 2020. Web. doi:10.1029/2020wr027883.
Nguyen, Hung T. T., Turner, Sean W. D., Buckley, Brendan M., & Galelli, Stefano. Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers. United States. https://doi.org/10.1029/2020wr027883
Nguyen, Hung T. T., Turner, Sean W. D., Buckley, Brendan M., and Galelli, Stefano. Sat . "Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers". United States. https://doi.org/10.1029/2020wr027883. https://www.osti.gov/servlets/purl/1781090.
@article{osti_1781090,
title = {Coherent Streamflow Variability in Monsoon Asia Over the Past Eight Centuries—Links to Oceanic Drivers},
author = {Nguyen, Hung T. T. and Turner, Sean W. D. and Buckley, Brendan M. and Galelli, Stefano},
abstractNote = {The Monsoon Asia region is home to ten of the world’s biggest rivers, supporting the lives of 1.7 billion people who rely on streamflow for water, energy, and food. Yet, a synoptic understanding of multi-centennial streamflow variability for this region is lacking. Here we produce the first large scale streamflow reconstruction over Monsoon Asia (63 stations in 16 countries), using a novel climate-informed dynamic algorithm that is skillful over 92% of the gauging stations. In this work, we show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans that exert their greatest influence over the Mekong and Chao Phraya basins. This work increases our understanding of streamflow variability over Monsoon Asia. We suggest that future water management in the region should be coordinated between basins, taking into account the states of the oceans.},
doi = {10.1029/2020wr027883},
journal = {Water Resources Research},
number = 12,
volume = 56,
place = {United States},
year = {Sat Sep 26 00:00:00 EDT 2020},
month = {Sat Sep 26 00:00:00 EDT 2020}
}

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