Technical Notes
Apr 25, 2019

Long-Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment

Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 7

Abstract

Diarrheal diseases, notably cholera, have been shown to be related to episodic seasonal variability in river discharge, predominantly low flows, in regions where water and sanitation infrastructure are inadequate. Forecasting river discharge in transboundary international basins a few months in advance remains elusive because the necessary geophysical data are unavailable or are not shared with stakeholders. We hypothesized that river discharge in large river basins is directly related to upstream water conditions that lead to generation of high and low flows. Using the Ganges-Brahmaputra-Meghna Rivers as an example and Bayesian regressive models, we showed that terrestrial water storage (TWS) anomalies from the Gravity Recovery and Climate Experiment (GRACE) can provide reliable estimates of flows, which are essential hydroclimatic variables for predicting endemic cholera, with an overall accuracy of 70% and up to 60 days in advance, without ancillary ground-based data.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

GRACE satellite data were obtained from National Aeronautics and Space Administration (NASA) servers (ftp://podaac-ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/). River discharge data can be requested from Institute of Water Modelling, Dhaka, Bangladesh (Email: [email protected]; phone: 880-288245901). Cholera epidemiological data are available from the weekly bulletin from International Centre for Diarrhoeal Disease Research, Bangladesh (http://www.icddrb.org/media-centre/weekly-bulletin).

Acknowledgments

This research is in part funded by a project from NASA (Grant No. 80NSSC18K0324).

References

Ahn, J., B. Mukherjee, M. Banerjee, and K. A. Cooney. 2009. “Bayesian inference for the stereotype regression model: Application to a case-control study of prostate cancer.” Stat. Med. 28 (25): 3139–3157. https://doi.org/10.1002/sim.3693.
Akanda, A. S., A. S. Jutla, M. Alam, G. C. De Magny, A. Siddique, R. B. Sack, A. Huq, R. R. Colwell, and S. Islam. 2011. “Hydroclimatic influences on seasonal and spatial cholera transmission cycles: Implications for public health intervention in the Bengal Delta: Hydroclimatic influences on seasonal cholera.” Water Resour. Res. 47 (3): W00H07. https://doi.org/10.1029/2010WR009914.
Akanda, A. S., A. S. Jutla, and R. R. Colwell. 2014. “Global diarrhoea action plan needs integrated climate-based surveillance.” Lancet Global Health 2 (2): 69–70. https://doi.org/10.1016/S2214-109X(13)70155-4.
Akanda, A., A. S. Jutla, D. Gute, T. Evans, and S. Islam. 2012. “Reinforcing cholera intervention through prediction-aided prevention.” Bull. World Health Organ. 90: 243–244. https://doi.org/10.2471/BLT.11.092189.
Akanda, A. S., A. S. Jutla, D. M. Gute, R. B. Sack, M. Alam, A. Huq, R. R. Colwell, and S. Islam. 2013. “Population vulnerability to biannual cholera outbreaks and associated macro-scale drivers in the Bengal delta.” Am. J. Trop. Med. Hyg. 89 (5): 950–959. https://doi.org/10.4269/ajtmh.12-0492.
Akanda, A. S., A. S. Jutla, and S. Islam. 2009. “Dual peak cholera transmission in Bengal delta: A hydroclimatological explanation.” Geophys. Res. Lett. 36 (19): L19401. https://doi.org/10.1029/2009GL039312.
Baldwin, S. A., and M. J. Larson. 2017. “An introduction to using Bayesian linear regression with clinical data.” Behav. Res. Therapy 98: 58–75. https://doi.org/10.1016/j.brat.2016.12.016.
Bartram, J. 2008. “Flowing away: Water and health opportunities.” Bull. World Health Organ. 86 (1): 2–3. https://doi.org/10.2471/BLT.07.049619.
Chevalier, L., B. Laignel, N. Massei, S. Munier, M. Becker, I. Turki, A. Coynel, and A. Cazenave. 2014. “Hydrological variability of major French rivers over recent decades, assessed from gauging station and GRACE observations.” Hydrol. Sci. J. 59 (10): 1844–1855. https://doi.org/10.1080/02626667.2013.866708.
Chu, P-S., and X. Zhao. 2007. “A Bayesian regression approach for predicting seasonal tropical cyclone activity over the Central North Pacific.” J. Clim. 20 (15): 4002–4013. https://doi.org/10.1175/JCLI4214.1.
Clark, J. S. 2005. “Why environmental scientists are becoming Bayesians.” Ecol. Lett. 8 (1): 2–14. https://doi.org/10.1111/j.1461-0248.2004.00702.x.
Drèze, J. H. 1977. “Bayesian regression analysis using poly-t densities.” J. Econometrics 6 (3): 329–354. https://doi.org/10.1016/0304-4076(77)90004-5.
Fornalski, K. W. 2015. “Applications of the robust Bayesian regression analysis.” Int. J. Soc. Syst. Sci. 7 (4): 314–333. https://doi.org/10.1504/IJSSS.2015.073223.
Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. 2014. Vol. 2 of Bayesian data analysis. Boca Raton, FL: CRC Press.
Hirpa, F. A., T. M. Hopson, T. De Groeve, G. R. Brakenridge, M. Gebremichael, and P. J. Restrepo. 2013. “Upstream satellite remote sensing for river discharge forecasting: Application to major rivers in South Asia.” Remote Sens. Environ. 131: 140–151. https://doi.org/10.1016/j.rse.2012.11.013.
Hopson, T. M., and P. J. Webster. 2010. “A 1–10-day ensemble forecasting scheme for the major river basins of Bangladesh: Forecasting severe floods of 2003–07.” J. Hydrometeorol. 11 (3): 618–641. https://doi.org/10.1175/2009JHM1006.1.
Jian, J., P. J. Webster, and C. D. Hoyos. 2009. “Large-scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time-scales.” Q. J. R. Meteorol. Soc. 135 (639): 353–370. https://doi.org/10.1002/qj.384.
Jutla, A. S., A. S. Akanda, J. K. Griffiths, R. Colwell, and S. Islam. 2011. “Warming oceans, phytoplankton, and river discharge: Implications for cholera outbreaks.” Am. J. Trop. Med. Hyg. 85 (2): 303–308. https://doi.org/10.4269/ajtmh.2011.11-0181.
Jutla, A. S., A. S. Akanda, and S. Islam. 2010. “Tracking cholera in coastal regions using satellite observations.” JAWRA J. Am. Water Resour. Assoc. 46 (4): 651–662. https://doi.org/10.1111/j.1752-1688.2010.00448.x.
Jutla, A. S., A. S. Akanda, and S. Islam. 2012. “Satellite remote sensing of space-time plankton variability in the Bay of Bengal: Connections to cholera outbreaks.” Remote Sens. Environ. 123: 196–206. https://doi.org/10.1016/j.rse.2012.03.005.
Jutla, A. S., A. S. Akanda, and S. Islam. 2013a. “A framework for predicting endemic cholera using satellite derived environmental determinants.” Environ. Model. Software 47: 148–158. https://doi.org/10.1016/j.envsoft.2013.05.008.
Jutla, A., E. Whitcombe, N. Hasan, B. Haley, A. Akanda, A. Huq, M. Alam, R. B. Sack, and R. Colwell. 2013b. “Environmental factors influencing epidemic cholera.” Am. J. Trop. Med. Hyg. 89 (5): 597–607. https://doi.org/10.4269/ajtmh.12-0721.
Landerer, F. W., and S. C. Swenson. 2012. “Accuracy of scaled GRACE terrestrial water storage estimates.” Water Resour. Res. 48 (4): W04531. https://doi.org/10.1029/2011WR011453.
Lee, H., R. E. Beighley, D. Alsdorf, H. C. Jung, C. K. Shum, J. Duan, J. Guo, D. Yamazaki, and K. Andreadis. 2011. “Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry.” Remote Sens. Environ. 115 (12): 3530–3538. https://doi.org/10.1016/j.rse.2011.08.015.
Li, Q., B. Zhong, Z. Luo, and C. Yao. 2016. “GRACE-based estimates of water discharge over the Yellow River basin.” Geod. Geodyn. 7 (3): 187–193. https://doi.org/10.1016/j.geog.2016.04.007.
Longini, I. M., M. Yunus, K. Zaman, A. K. Siddique, R. B. Sack, and A. Nizam. 2002. “Epidemic and endemic cholera trends over a 33-year period in Bangladesh.” J. Infect. Dis. 186 (2): 246–251. https://doi.org/10.1086/341206.
Luo, Z., C. Yao, Q. Li, and Z. Huang. 2016. “Terrestrial water storage changes over the Pearl River Basin from GRACE and connections with Pacific climate variability.” Geod. Geodyn. 7 (3): 171–179. https://doi.org/10.1016/j.geog.2016.04.008.
Ntzoufras, I. 2009. Bayesian modeling using WinBUGS. Hoboken, NJ: Wiley.
Reager, J. T., A. C. Thomas, E. A. Sproles, M. Rodell, H. K. Beaudoing, B. Li, and J. S. Famiglietti. 2015. “Assimilation of GRACE terrestrial water storage observations into a land surface model for the assessment of regional flood potential.” Remote Sens. 7 (11): 14663–14679. https://doi.org/10.3390/rs71114663.
Shukla, J., and D. A. Paolino. 1983. “The southern oscillation and long-range forecasting of the summer monsoon rainfall over India.” Mon. Weather Rev. 111 (9): 1830–1837. https://doi.org/10.1175/1520-0493(1983)111%3C1830:TSOALR%3E2.0.CO;2.
Swenson, S., J. Wahr, and P. C. D. Milly. 2003. “Estimated accuracies of regional water storage variations inferred from the gravity recovery and climate experiment (GRACE): Regional water storage estimates from GRACE.” Water Resour. Res. 39 (8): SWC11. https://doi.org/10.1029/2002WR001808.
Sun, Z., X. Zhu, Y. Pan, and J. Zhang. 2017. “Assessing terrestrial water storage and flood potential using GRACE data in the Yangtze River basin, China.” Remote Sens. 9 (10): 1011. https://doi.org/10.3390/rs9101011.
Syed, T. H., J. S. Famiglietti, J. Chen, M. Rodell, S. I. Seneviratne, P. Viterbo, and C. R. Wilson. 2005. “Total basin discharge for the Amazon and Mississippi River basins from GRACE and a land-atmosphere water balance.” Geophys. Res. Lett. 32 (24): L24404. https://doi.org/10.1029/2005GL024851.
Torrence, C., and P. J. Webster. 1999. “Interdecadal changes in the ENSO-monsoon system.” J. Clim. 12 (8): 2679–2690. https://doi.org/10.1175/1520-0442(1999)012%3C2679:ICITEM%3E2.0.CO;2.
Wang, S., F. Zhou, H. A. Russell, R. Huang, and Y. Shen. 2016. “Peak river flows in cold regions—Drivers and modelling using GRACE satellite observations and temperature data.” Hydrol. Earth Syst. Sci. Discuss https://doi.org/10.5194/hess-2016-117.
Webster, P. J., and C. Hoyos. 2004. “Prediction of monsoon rainfall and river discharge on 15–30-day time scales.” Bull. Am. Meteorol. Soc. 85 (11): 1745–1765. https://doi.org/10.1175/BAMS-85-11-1745.
Whitaker, D. W., S. A. Wasimi, and S. Islam. 2001. “The El Nino southern oscillation and long-range forecasting of flows in the Ganges.” Int. J. Climatol. 21 (1): 77–87. https://doi.org/10.1002/joc.583.

Information & Authors

Information

Published In

Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 145Issue 7July 2019

History

Received: Jan 18, 2018
Accepted: Nov 7, 2018
Published online: Apr 25, 2019
Published in print: Jul 1, 2019
Discussion open until: Sep 25, 2019

Permissions

Request permissions for this article.

Authors

Affiliations

Graduate Student Researcher, Dept. of Civil and Environmental Engineering, West Virginia Univ., Morgantown, WV 26505. ORCID: https://orcid.org/0000-0001-9994-1094
Moiz Usmani
Graduate Student Researcher, Dept. of Civil and Environmental Engineering, West Virginia Univ., Morgantown, WV 26505.
Ali Akanda
Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Rhode Island, Kingston, RI 02881.
Wahid Palash
Scientist, Institute of Water Modelling, Dhaka, Bangladesh.
Yongxuan Gao
Water Resources Control Engineer, Division of Water Rights, California State Water Resources Control Board, 1001 I St., Sacramento, CA 95814.
Anwar Huq
Professor, Maryland Pathogen Research Institute, Univ. of Maryland, College Park, MD 20740.
Rita Colwell
Professor, Maryland Pathogen Research Institute, Center for Bioinformatics and Computational Biology, Univ. of Maryland, College Park, MD 20740.
Antarpreet Jutla, M.ASCE [email protected]
Associate Professor, Dept. of Civil and Environmental Engineering, West Virginia Univ., Morgantown, WV 26505 (corresponding author). Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share