Automation and human expertise in operational river forecasting.

Automatisation et expertise humaine dans la prévision opérationnelle de débits.

Pagano, T. C. ; Pappenberger, F. ; Wood, A. W. ; Ramos, M.-H. ; Persson, A. ; Anderson, B.

Type de document
Article de revue scientifique à comité de lecture
Langue
Anglais
Affiliation de l'auteur
BUREAU OF METEOROLOGY MELBOURNE AUS ; ECMWF EUROPEAN CENTRE OF MEDIUM RANGE WEATHER FORECASTS GBR ; NCAR BOULDER USA ; IRSTEA ANTONY UR HBAN FRA ; UPPSALA UNIVERSITET SWE ; BUREAU OF METEOROLOGY MELBOURNE AUS
Année
2016
Résumé / Abstract
Increased automation and use of computer-aided decision support systems are attractive options for hydrologic forecasting agencies faced with growing product complexity and institutional resourcing pressures. Although the hydrologic literature has been nearly silent on the roles of expertise and automation in forecasting, other disciplines such as meteorology have had decades of open discussion on the topic. To address the lack of dialogue in hydrology on automation, this article seeks to contextualize relevant findings from similar disciplines, including meteorology, psychology, decision support systems, and interface design. We predict which aspects of operational hydrology have the greatest chance for successfully increasing automation in the near future. Some applications have employed higher levels of automation, notably flash flood forecasting which requires rapid response times, and extended prediction which requires heavy emphasis on uncertainty quantification. Short-range flood forecasting may be more challenging to automate and traditionally has been less automated than other types of forecasts, partly because of existing practices of interfacing with meteorologists and water system operators, and the difficulties in modeling human impacts on the water cycle. Overall, we suggest that the design of computer-aided decision support systems for forecasting systems should consider three factors: (1) processes change under automation and people may require new roles; (2) automation changes the way people behave, sometimes negatively; and (3) people may not have accurate perceptions of the quality of the automated guidance. Seven lessons learned from automation in meteorology are highlighted and translated into a hydrologic forecasting context, leading to a set of recommendations for how to make best use of expertise in increasingly automated systems.
Source
Wires Water, vol. 3, num. 5, p. 692 - 705

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