Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale
Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale
A novel approach of combining regionalization and satellite observations of various hydrological variables were employed to significantly improve prediction of streamflow signatures at “geopolitically ungauged” basins. Using the proposed step-wise physiography and climate-based regionalization approach, the model performance at ungauged basins reached 80% of performance of locally calibrated parameters and significantly outperformed the global regionalization parameters. The proposed water level based flow correlation was found to help diagnose models and outperform the existing performance metrics of simulated water levels at ungauged basins. The study also set up the first multi-national, multi-catchment hydrological model in the Greater Mekong region, the top global biodiversity and major disaster risk hotspot in the world through sequential and iterative refinement of the existing global hydrological model. New model setup or existing models in the poorly-gauged and ungauged basins could benefit from the proposed approach to predict and evaluate models at ungauged basins.
Altimetry, Catchment model, Flow correlation, Mekong, Regionalization, Satellite observations
Du, Tien
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Lee, Hyongki
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Bui, Duong
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Arheimer, Berit
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Li, Hong-Yi
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Olsson, Jonas
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Darby, Stephen
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Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Kim, Donghwan
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Hwang, Euiho
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September 2020
Du, Tien
8e10bd8b-247c-403d-b11e-539084cc42fd
Lee, Hyongki
9863190c-b68f-4bf2-bc6d-076ffc651089
Bui, Duong
47393e2a-e8a5-4357-8b8c-72582ad57f26
Arheimer, Berit
18fed6be-e59e-49b1-8de9-04d7839b8a58
Li, Hong-Yi
d231fb5c-ded5-4525-9dd7-1c0363acdc2b
Olsson, Jonas
48569c87-7cc2-4352-8b02-b2530513027c
Darby, Stephen
4c3e1c76-d404-4ff3-86f8-84e42fbb7970
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Kim, Donghwan
29578b04-4b4b-4114-a6ef-acae8b4b6e45
Hwang, Euiho
f918d661-c560-4698-91e3-f70dcb405540
Du, Tien, Lee, Hyongki, Bui, Duong, Arheimer, Berit, Li, Hong-Yi, Olsson, Jonas, Darby, Stephen, Sheffield, Justin, Kim, Donghwan and Hwang, Euiho
(2020)
Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale.
Journal of Hydrology, 588, [125016].
(doi:10.1016/j.jhydrol.2020.125016).
Abstract
A novel approach of combining regionalization and satellite observations of various hydrological variables were employed to significantly improve prediction of streamflow signatures at “geopolitically ungauged” basins. Using the proposed step-wise physiography and climate-based regionalization approach, the model performance at ungauged basins reached 80% of performance of locally calibrated parameters and significantly outperformed the global regionalization parameters. The proposed water level based flow correlation was found to help diagnose models and outperform the existing performance metrics of simulated water levels at ungauged basins. The study also set up the first multi-national, multi-catchment hydrological model in the Greater Mekong region, the top global biodiversity and major disaster risk hotspot in the world through sequential and iterative refinement of the existing global hydrological model. New model setup or existing models in the poorly-gauged and ungauged basins could benefit from the proposed approach to predict and evaluate models at ungauged basins.
Text
Du_et_all_JOH_Proof1
- Accepted Manuscript
More information
e-pub ahead of print date: 27 April 2020
Published date: September 2020
Additional Information:
Funding Information:
This study is supported by NASA’s Applied Sciences Program for SWOT Science Team ( NNX16AQ33G ); NASA's GEO Program ( 80NSSC18K0423 ); Vingroup Innovation Foundation (VINIF.2019.DA17); the Greater Mekong HYPE Study of the ECMWF COPERNICUS contract (C3S_422_Lot1_SMHI); NASA's SERVIR Program (80NSSC20K0152); Vietnam National Foundation for Science and Technology Development (NAFOSTED ) and the United Kingdom's Natural Environment Research Council ( NERC ) (NE/S002847/1); USAID’s PEER Project (AID-OAA-A-11-00012); and South Korea Ministry of Environment’s Demand Responsive Water Supply Service Program (2019002650004). Especially, the authors would like to express our gratitude to SMHI hydrological research unit and water monitoring and forecasting team at NAWAPI, where various prior common works over multiple years on the regional modelling platform were done to make this study possible. We would like to thank Dr. Phil Graham, Dr. René Capell and Johan Strömqvist at SMHI for supporting with HYPE model setup, Kel Markert at the University of Alabama in Huntsville for helping with Google Earth Engine coding, and data providers listed in Table 1 for providing us important resources and data to undertake this work.
Publisher Copyright:
© 2020 Elsevier B.V.
Keywords:
Altimetry, Catchment model, Flow correlation, Mekong, Regionalization, Satellite observations
Identifiers
Local EPrints ID: 441028
URI: http://eprints.soton.ac.uk/id/eprint/441028
ISSN: 0022-1694
PURE UUID: 56ca822b-1146-4208-815d-221a1afb7e73
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Date deposited: 27 May 2020 16:55
Last modified: 17 Mar 2024 05:34
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Contributors
Author:
Tien Du
Author:
Hyongki Lee
Author:
Duong Bui
Author:
Berit Arheimer
Author:
Hong-Yi Li
Author:
Jonas Olsson
Author:
Donghwan Kim
Author:
Euiho Hwang
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