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Streamflow prediction in highly regulated, transboundary watersheds using multi-basin modelling and remote sensing imagery

Streamflow prediction in highly regulated, transboundary watersheds using multi-basin modelling and remote sensing imagery
Streamflow prediction in highly regulated, transboundary watersheds using multi-basin modelling and remote sensing imagery

Despite the potential of remote sensing for monitoring reservoir operation, few studies have investigated the extent to which reservoir releases can be inferred across different spatial and temporal scales. Through evaluating 21 reservoirs in the highly regulated Greater Mekong region, remote sensing imagery was found to be useful in estimating daily storage volumes for within-year and over-year reservoirs (correlation coefficients [CC] ≥ 0.9, normalized root mean squared error [NRMSE] ≤ 31%), but not for run-of-river reservoirs (CC < 0.4, 40% ≤ NRMSE ≤ 270%). Given a large gap in the number of reservoirs between global and local databases, the proposed framework can improve representation of existing reservoirs in the global reservoir database and thus human impacts in hydrological models. Adopting an Integrated Reservoir Operation Scheme within a multi-basin model was found to overcome the limitations of remote sensing and improve streamflow prediction at ungauged cascade reservoir systems where previous modeling approaches were unsuccessful. As a result, daily regulated streamflow was predicted competently across all types of reservoirs (median values of CC = 0.65, NRMSE = 8%, and Kling-Gupta efficiency [KGE] = 0.55) and downstream hydrological stations (median values of CC = 0.94, NRMSE = 8%, and KGE = 0.81). The findings are valuable for helping to understand the impacts of reservoirs and dams on streamflow and for developing more useful adaptation measures to extreme events in data sparse river basins.

Mekong and Vietnam, multi-basin model, regulated streamflow, remote sensing imagery, reservoir operation, transboundary
0043-1397
Du, Tien L. T.
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Lee, Hyongki
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Bui, Duong D.
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Graham, L. Phil
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Darby, Stephen D.
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Pechlivandis, Ilias G.
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Leyland, Julian
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Biswas, Nishan K.
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Choi, Gyewoon
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Batelaan, Okke
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Bui, Thao T. P.
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Do, Son K.
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Tran, Tinh V.
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Nguyen, Hoa Thi
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Hwang, Euiho
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Du, Tien L. T.
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Lee, Hyongki
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Bui, Duong D.
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Graham, L. Phil
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Darby, Stephen D.
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Pechlivandis, Ilias G.
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Leyland, Julian
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Biswas, Nishan K.
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Choi, Gyewoon
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Batelaan, Okke
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Bui, Thao T. P.
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Do, Son K.
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Tran, Tinh V.
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Nguyen, Hoa Thi
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Hwang, Euiho
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Du, Tien L. T., Lee, Hyongki, Bui, Duong D., Graham, L. Phil, Darby, Stephen D., Pechlivandis, Ilias G., Leyland, Julian, Biswas, Nishan K., Choi, Gyewoon, Batelaan, Okke, Bui, Thao T. P., Do, Son K., Tran, Tinh V., Nguyen, Hoa Thi and Hwang, Euiho (2022) Streamflow prediction in highly regulated, transboundary watersheds using multi-basin modelling and remote sensing imagery. Water Resources Research, 58 (3), [e2021WR031191]. (doi:10.1029/2021WR031191).

Record type: Article

Abstract

Despite the potential of remote sensing for monitoring reservoir operation, few studies have investigated the extent to which reservoir releases can be inferred across different spatial and temporal scales. Through evaluating 21 reservoirs in the highly regulated Greater Mekong region, remote sensing imagery was found to be useful in estimating daily storage volumes for within-year and over-year reservoirs (correlation coefficients [CC] ≥ 0.9, normalized root mean squared error [NRMSE] ≤ 31%), but not for run-of-river reservoirs (CC < 0.4, 40% ≤ NRMSE ≤ 270%). Given a large gap in the number of reservoirs between global and local databases, the proposed framework can improve representation of existing reservoirs in the global reservoir database and thus human impacts in hydrological models. Adopting an Integrated Reservoir Operation Scheme within a multi-basin model was found to overcome the limitations of remote sensing and improve streamflow prediction at ungauged cascade reservoir systems where previous modeling approaches were unsuccessful. As a result, daily regulated streamflow was predicted competently across all types of reservoirs (median values of CC = 0.65, NRMSE = 8%, and Kling-Gupta efficiency [KGE] = 0.55) and downstream hydrological stations (median values of CC = 0.94, NRMSE = 8%, and KGE = 0.81). The findings are valuable for helping to understand the impacts of reservoirs and dams on streamflow and for developing more useful adaptation measures to extreme events in data sparse river basins.

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Accepted/In Press date: 4 March 2022
e-pub ahead of print date: 11 March 2022
Published date: 11 March 2022
Additional Information: Funding Information: This study is supported by NASA’s Applied Sciences Program for GEOGloWS (80NSSC18K0423) and SERVIR Program (80NSSC20K0152); Vingroup Innovation Foundation (VINIF.2019.DA17); Vietnam National Foundation for Science and Technology Development and the United Kingdom’s Natural Environment Research Council (105.08‐2020.11 and NE/S002847/1); Korea Ministry of Environment under the Demand Responsive Water Supply Service Program (2019002650004). Furthermore, the authors would like to express our gratitude to Water Resources Monitoring Department at NAWAPI, where various prior works with national and international partners over multiple years on the regional HYPE modeling platform and extensive in situ data collection were done to make this study possible. Especially, we would like to thank Johan Strömqvist, Kristina Isberg, and Nguyen T. P. Hoa for supporting the model setup and data providers listed in Table S1 in Supporting Information S1 for providing us important resources to undertake this work. Publisher Copyright: © 2022. The Authors.
Keywords: Mekong and Vietnam, multi-basin model, regulated streamflow, remote sensing imagery, reservoir operation, transboundary

Identifiers

Local EPrints ID: 455994
URI: http://eprints.soton.ac.uk/id/eprint/455994
ISSN: 0043-1397
PURE UUID: e3985a6a-e987-439b-b5a6-56914abdfa59
ORCID for Stephen D. Darby: ORCID iD orcid.org/0000-0001-8778-4394
ORCID for Julian Leyland: ORCID iD orcid.org/0000-0002-3419-9949

Catalogue record

Date deposited: 11 Apr 2022 18:08
Last modified: 13 Sep 2022 01:40

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Contributors

Author: Tien L. T. Du
Author: Hyongki Lee
Author: Duong D. Bui
Author: L. Phil Graham
Author: Ilias G. Pechlivandis
Author: Julian Leyland ORCID iD
Author: Nishan K. Biswas
Author: Gyewoon Choi
Author: Okke Batelaan
Author: Thao T. P. Bui
Author: Son K. Do
Author: Tinh V. Tran
Author: Hoa Thi Nguyen
Author: Euiho Hwang

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