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Multisource estimation of long-term terrestrial water budget for major global river basins

Multisource estimation of long-term terrestrial water budget for major global river basins
Multisource estimation of long-term terrestrial water budget for major global river basins

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984-2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

Climate variability, Error analysis, Hydrologic cycle, Kalman filters, Water budget
0894-8755
3191-3206
Pan, Ming
10c372fa-0e0e-4eb5-b95b-06a8f9786fc8
Sahoo, Alok K.
ac3309f9-a1aa-4b13-abef-e5bd9080d8ea
Troy, Tara J.
0f42a33e-d70e-4f52-a559-718f4a25d8d8
Vinukollu, Raghuveer K.
8ff428ba-17eb-4318-9a82-3ed70df6d013
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, And Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Pan, Ming
10c372fa-0e0e-4eb5-b95b-06a8f9786fc8
Sahoo, Alok K.
ac3309f9-a1aa-4b13-abef-e5bd9080d8ea
Troy, Tara J.
0f42a33e-d70e-4f52-a559-718f4a25d8d8
Vinukollu, Raghuveer K.
8ff428ba-17eb-4318-9a82-3ed70df6d013
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, And Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf

Pan, Ming, Sahoo, Alok K., Troy, Tara J., Vinukollu, Raghuveer K., Sheffield, Justin and Wood, And Eric F. (2012) Multisource estimation of long-term terrestrial water budget for major global river basins. Journal of Climate, 25 (9), 3191-3206. (doi:10.1175/JCLI-D-11-00300.1).

Record type: Article

Abstract

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984-2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

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More information

Published date: 1 May 2012
Keywords: Climate variability, Error analysis, Hydrologic cycle, Kalman filters, Water budget

Identifiers

Local EPrints ID: 480756
URI: http://eprints.soton.ac.uk/id/eprint/480756
ISSN: 0894-8755
PURE UUID: 2f7e3459-ec74-4fcb-92d4-f6c5f8be48d4
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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

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Contributors

Author: Ming Pan
Author: Alok K. Sahoo
Author: Tara J. Troy
Author: Raghuveer K. Vinukollu
Author: And Eric F. Wood

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