TY - JOUR
T1 - Detailed overview of the multimodel multiproduct streamflow forecasting platform
AU - Roy, Tirthankar
AU - Valdés, Juan B.
AU - Serrat-Capdevila, Aleix
AU - Durcik, Matej
AU - Demaria, Eleonora M.C.
AU - Valdés-Pineda, Rodrigo
AU - Gupta, Hoshin V.
N1 - Funding Information:
This work was supported by ICIWaRM-UNESCO; NASA-USAID SERVIR Program: [grant number 11-SERVIR11-58]. The main support for this work was provided by the NASA-USAID SERVIR Program through award number 11-SERVIR11-58. International Center for Integrated Water Resources Management (ICIWaRM-UNESCO) provided partial support for initial prototype development. The conversion of scripts from MATLAB to Python in MMSF-Basic was carried out by Forest Carter, School of Geography and Development, University of Arizona. The need for this paper became evident during a training workshop on the operational implementation of the MMSF Platform at RCMRD, Kenya. Faith Mitheu and Steven Otieno from RCMRD provided valuable feedback from an application point of view. We thank Ashutosh Limaye, Begum Rabeya Rushi, and Emily Adams from the SERVIR team for their support.
Funding Information:
The main support for this work was provided by the NASA-USAID SERVIR Program through award number 11-SERVIR11-58. International Center for Integrated Water Resources Management (ICIWaRM-UNESCO) provided partial support for initial prototype development. The conversion of scripts from MATLAB to Python in MMSF-Basic was carried out by Forest Carter, School of Geography and Development, University of Arizona. The need for this paper became evident during a training workshop on the operational implementation of the MMSF Platform at RCMRD, Kenya. Faith Mitheu and Steven Otieno from RCMRD provided valuable feedback from an application point of view. We thank Ashutosh Limaye, Begum Rabeya Rushi, and Emily Adams from the SERVIR team for their support.
Publisher Copyright:
© 2020 IAHR and WCCE.
PY - 2020/12
Y1 - 2020/12
N2 - We present a detailed overview of the Multi-model Multi-product Streamflow Forecasting (MMSF) Platform, which has been developed recently at the University of Arizona under the NASA SERVIR Program, to ease its operational implementation. The platform is based on the use of multiple hydrologic models, satellite-based precipitation products, advanced bias correction schemes, model calibration, and probabilistic model averaging, with the goal of improving forecast accuracy and better-characterizing forecast uncertainties, especially in poorly gauged basins. This paper includes a brief description of the platform, followed by all the relevant information a user would need to implement the platform on any new river basin.
AB - We present a detailed overview of the Multi-model Multi-product Streamflow Forecasting (MMSF) Platform, which has been developed recently at the University of Arizona under the NASA SERVIR Program, to ease its operational implementation. The platform is based on the use of multiple hydrologic models, satellite-based precipitation products, advanced bias correction schemes, model calibration, and probabilistic model averaging, with the goal of improving forecast accuracy and better-characterizing forecast uncertainties, especially in poorly gauged basins. This paper includes a brief description of the platform, followed by all the relevant information a user would need to implement the platform on any new river basin.
KW - MMSF platform
KW - operational forecasting
KW - satellite precipitation
KW - streamflow forecasting
KW - streamflow monitoring
UR - http://www.scopus.com/inward/record.url?scp=85091827182&partnerID=8YFLogxK
U2 - 10.1080/23249676.2020.1799442
DO - 10.1080/23249676.2020.1799442
M3 - Article
AN - SCOPUS:85091827182
VL - 8
SP - 277
EP - 289
JO - Journal of Applied Water Engineering and Research
JF - Journal of Applied Water Engineering and Research
SN - 2324-9676
IS - 4
ER -