Detailed overview of the multimodel multiproduct streamflow forecasting platform

Abstract

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.

KEYWORDS:

• MMSF platform
• streamflow monitoring
• streamflow forecasting
• satellite precipitation
• operational forecasting

Acknowledgment

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.

Additional information

Funding

This work was supported by ICIWaRM-UNESCO; NASA-USAID SERVIR Program: [grant number 11-SERVIR11-58].

Notes on contributors

Tirthankar Roy

Tirthankar Roy is an Assistant Professor in the Department of Civil and Environmental Engineering, University of Nebraska-Lincoln. He holds a Ph.D. in Hydrology from the University of Arizona. He serves on the Early Career Committee of the International Association of Hydrological Sciences and the Hydrological Uncertainty Technical committee of the American Geophysical Union. His research interests include satellite remote sensing applications in hydrology, hydrologic extremes, catchment hydrology, land-atmospheric interactions, statistics, and machine learning, and water resources management.

Juan B. Valdés

Juan B. Valdés is an emeritus professor in the Department of Hydrology and Atmospheric Sciences at the University of Arizona. He holds a Ph.D. degree in water resources from MIT and has contributed to the field of hydrology for over four decades. His main areas of research interest are stochastic hydrology and water resources management.

Aleix Serrat-Capdevila

Aleix Serrat-Capdevila is a senior water resources management specialist at the World Bank, within the Water Global Practice since 2016, where he manages analytical and operational projects. During two years in Washington DC, he managed a technical assistance program (the Water Expert Team) and the Global Initiative on Remote Sensing for Water Resources Management. He is based in Luanda (Angola) since 2018. Before joining the World Bank, he was a Research Professor at the University of Arizona, working on applied water management projects to bring science closer to practical water management problems.

Matej Durcik

Matej Durcik is a researcher in Biosphere 2 at the University of Arizona. His work focuses on applications of geo-informatics technologies in environmental sciences, database systems and design, spatial and temporal data analysis, and assimilation, distributed hydrologic modeling, physical hydrologic processes, radiation, and environmental physics, and climate variability.

Eleonora M. C. Demaria

Eleonora M. C. Demaria is a hydrologist with the Pima County Regional Flood Control District in Arizona (USA). She obtained a B.S. in Water Resources Engineering from the Universidad Nacional del Litoral in Argentina, an M.S. in Meteorology from the University of Utah (USA), and a Ph.D. in Hydrology from the University of Arizona (USA). Her areas of interest are climate change impacts on water resources and the use of satellite estimations for flood forecast.

Rodrigo Valdés-Pineda

Rodrigo Valdés-Pineda has vast experience working in the field of hydrological modeling, water resources, and watershed management. His most recent areas of research have been focused on the implementation of remote sensing applications for operational hydrologic forecasting. His research and technical works have included the development of real-time, short-range to medium-range (SR2MR) and seasonal hydrological forecasting systems (HFS) utilizing satellite precipitation products (SPPs) and global/regional climate model (GCMs and RCMs) outputs in the pilot basins of Africa, South America, Central America, and the United States.

Hoshin V. Gupta

Hoshin V. Gupta is a Regent's Professor in the Department of Hydrology and Atmospheric Sciences. His research interest is in combining Physics-Based Knowledge with Machine Learning (via Information Theory) to develop Earth & Environmental Systems Models that can learn from interactions with the environment. In 2017 and 2018, he was ranked in the top 1% on the Clarivate “Highly Cited Researchers List” for Environment/Ecology. He is also a Fellow of the American Geophysical Union and recipient of the American Meteorological Society's RE Horton Lecture Award (2017) and the European Geosciences Union's Dalton Medal (2014).