A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting
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Roy_et_al-2017-Water_Resources ...
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FInal Published Version
Affiliation
Univ Arizona, Dept Hydrol & Atmospher SciIssue Date
2017-01Keywords
streamflow forecastingsatellite precipitation products
bias correction
model averaging
uncertainty analysis
real-time monitoring
MMSF
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AMER GEOPHYSICAL UNIONCitation
A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting 2017, 53 (1):376 Water Resources ResearchJournal
Water Resources ResearchRights
© 2016. American Geophysical Union. All Rights ReservedCollection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We develop and test a probabilistic real-time streamflow-forecasting platform, Multimodel and Multiproduct Streamflow Forecasting (MMSF), that uses information provided by a suite of hydrologic models and satellite precipitation products (SPPs). The SPPs are bias-corrected before being used as inputs to the hydrologic models, and model calibration is carried out independently for each of the model-product combinations (MPCs). Forecasts generated from the calibrated models are further bias-corrected to compensate for the deficiencies within the models, and then probabilistically merged using a variety of model averaging techniques. Use of bias-corrected SPPs in streamflow forecasting applications can overcome several issues associated with sparsely gauged basins and enable robust forecasting capabilities. Bias correction of streamflow significantly improves the forecasts in terms of accuracy and precision for all different cases considered. Results show that the merging of individual forecasts from different MPCs provides additional improvements. All the merging techniques applied in this study produce similar results, however, the Inverse Weighted Averaging (IVA) proves to be slightly superior in most cases. We demonstrate the implementation of the MMSF platform for real-time streamflow monitoring and forecasting in the Mara River basin of Africa (Kenya & Tanzania) in order to provide improved monitoring and forecasting tools to inform water management decisions.Note
6 month embargo; First published: 17 January 2017ISSN
00431397Version
Final published versionSponsors
NASA-USAID [11-SERVIR11-58]; International Center for Integrated Water Resources Management (ICIWaRM-UNESCO); Australian Research Council through the Centre of Excellence for Climate System Science [CE110001028]; EU [INCO-20011-7.6, 294947]Additional Links
http://doi.wiley.com/10.1002/2016WR019752ae974a485f413a2113503eed53cd6c53
10.1002/2016WR019752