Challenges of Operational River Forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover...

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Bibliographic Details
Published in:Journal of hydrometeorology 2014-08, Vol.15 (4), p.1692-1707
Main Authors: Pagano, Thomas C., Wood, Andrew W., Ramos, Maria-Helena, Cloke, Hannah L., Pappenberger, Florian, Clark, Martyn P., Cranston, Michael, Kavetski, Dmitri, Mathevet, Thibault, Sorooshian, Soroosh, Verkade, Jan S.
Format: Article
Language:English
Subjects:
Environmental Sciences
Flood predictions
Floods
Forecasting models
Freshwater
Hydrological modeling
Hydrology
Meteorology
Numerical weather forecasting
Probability forecasts
Stream flow
Weather forecasting
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Summary:Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1)making the most of available data, 2)making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
ISSN:1525-755X
1525-7541
DOI:10.1175/JHM-D-13-0188.1