Premium
Automation and human expertise in operational river forecasting
Author(s) -
Pagano Thomas C.,
Pappenberger Florian,
Wood Andrew W.,
Ramos MariaHelena,
Persson Anders,
Anderson Brett
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1163
Subject(s) - automation , interfacing , context (archaeology) , computer science , decision support system , set (abstract data type) , operations research , systems engineering , data science , risk analysis (engineering) , engineering , artificial intelligence , business , paleontology , biology , mechanical engineering , computer hardware , programming language
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. WIREs Water 2016, 3:692–705. doi: 10.1002/wat2.1163 This article is categorized under: Engineering Water > Methods Science of Water > Water Extremes