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Water balance in the amazon basin from a land surface model ensemble

Water balance in the amazon basin from a land surface model ensemble
Water balance in the amazon basin from a land surface model ensemble

Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26mmday-1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

Amazon region, Hydrologic models, Land surface model, Runoff
1525-755X
2586-2614
Getirana, Augusto C.V.
bf7518cc-80a4-4441-9ced-ad0177643405
Dutra, Emanuel
7324810e-fd8c-4469-8f05-ed454254c3da
Guimberteau, Matthieu
3ae4bf35-ec28-487c-8f3b-3dfc910fe638
Kam, Jonghun
2ca1444e-be4f-4250-9fa5-c5b9dca224fe
Li, Hong Yi
6877457e-bf1a-4b93-9619-e66f3abb105c
Decharme, Bertrand
3bf27992-c623-4728-8877-ebb540f2b18e
Zhang, Zhengqiu
a7f2e402-2f87-40c5-9cc0-d67656bc9fde
Ducharne, Agnes
e97a13a6-ac4d-482d-85e8-82c9f2971122
Boone, Aaron
bdb9e6cb-430e-4da6-9b54-8338d8cabb1d
Balsamo, Gianpaolo
1c4acbb3-5f48-48cb-b06a-aa405ff287c8
Rodell, Matthew
8601b311-4098-4f9d-a19f-925ccea0a813
Toure, Ally M.
1bb2cfce-09f0-44fa-97c6-67725337abeb
Xue, Yongkang
148ff3e3-c7a7-45e6-84ba-f188b75652b3
Peters-Lidard, Christa D.
ec52af50-c979-4df9-a8ee-0232a9d79878
Kumar, Sujay V.
ce78fcf6-cc8b-4b5c-a18a-1bb837dbcf0b
Arsenault, Kristi
0cdf9440-a813-4e68-a466-69d175341812
Drapeau, Guillaume
061f7529-85e1-42e4-b7e6-02ccd4014679
Leung, L. Ruby
797fe02c-fba7-4618-a788-c7dff944243a
Ronchail, Josyane
8db43b09-7d0b-4c73-8049-c2767bd3bd48
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Getirana, Augusto C.V.
bf7518cc-80a4-4441-9ced-ad0177643405
Dutra, Emanuel
7324810e-fd8c-4469-8f05-ed454254c3da
Guimberteau, Matthieu
3ae4bf35-ec28-487c-8f3b-3dfc910fe638
Kam, Jonghun
2ca1444e-be4f-4250-9fa5-c5b9dca224fe
Li, Hong Yi
6877457e-bf1a-4b93-9619-e66f3abb105c
Decharme, Bertrand
3bf27992-c623-4728-8877-ebb540f2b18e
Zhang, Zhengqiu
a7f2e402-2f87-40c5-9cc0-d67656bc9fde
Ducharne, Agnes
e97a13a6-ac4d-482d-85e8-82c9f2971122
Boone, Aaron
bdb9e6cb-430e-4da6-9b54-8338d8cabb1d
Balsamo, Gianpaolo
1c4acbb3-5f48-48cb-b06a-aa405ff287c8
Rodell, Matthew
8601b311-4098-4f9d-a19f-925ccea0a813
Toure, Ally M.
1bb2cfce-09f0-44fa-97c6-67725337abeb
Xue, Yongkang
148ff3e3-c7a7-45e6-84ba-f188b75652b3
Peters-Lidard, Christa D.
ec52af50-c979-4df9-a8ee-0232a9d79878
Kumar, Sujay V.
ce78fcf6-cc8b-4b5c-a18a-1bb837dbcf0b
Arsenault, Kristi
0cdf9440-a813-4e68-a466-69d175341812
Drapeau, Guillaume
061f7529-85e1-42e4-b7e6-02ccd4014679
Leung, L. Ruby
797fe02c-fba7-4618-a788-c7dff944243a
Ronchail, Josyane
8db43b09-7d0b-4c73-8049-c2767bd3bd48
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b

Getirana, Augusto C.V., Dutra, Emanuel, Guimberteau, Matthieu, Kam, Jonghun, Li, Hong Yi, Decharme, Bertrand, Zhang, Zhengqiu, Ducharne, Agnes, Boone, Aaron, Balsamo, Gianpaolo, Rodell, Matthew, Toure, Ally M., Xue, Yongkang, Peters-Lidard, Christa D., Kumar, Sujay V., Arsenault, Kristi, Drapeau, Guillaume, Leung, L. Ruby, Ronchail, Josyane and Sheffield, Justin (2014) Water balance in the amazon basin from a land surface model ensemble. Journal of Hydrometeorology, 15 (6), 2586-2614. (doi:10.1175/JHM-D-14-0068.1).

Record type: Article

Abstract

Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26mmday-1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

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More information

Published date: 2014
Additional Information: Publisher Copyright: © 2014 American Meteorological Society.
Keywords: Amazon region, Hydrologic models, Land surface model, Runoff

Identifiers

Local EPrints ID: 480792
URI: http://eprints.soton.ac.uk/id/eprint/480792
ISSN: 1525-755X
PURE UUID: 6b309901-66e0-431b-9f33-788b42756534
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 09 Aug 2023 17:14
Last modified: 17 Mar 2024 03:40

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Contributors

Author: Augusto C.V. Getirana
Author: Emanuel Dutra
Author: Matthieu Guimberteau
Author: Jonghun Kam
Author: Hong Yi Li
Author: Bertrand Decharme
Author: Zhengqiu Zhang
Author: Agnes Ducharne
Author: Aaron Boone
Author: Gianpaolo Balsamo
Author: Matthew Rodell
Author: Ally M. Toure
Author: Yongkang Xue
Author: Christa D. Peters-Lidard
Author: Sujay V. Kumar
Author: Kristi Arsenault
Author: Guillaume Drapeau
Author: L. Ruby Leung
Author: Josyane Ronchail

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