SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:DiVA.org:uu-307871"
 

Sökning: id:"swepub:oai:DiVA.org:uu-307871" > Automation and huma...

Automation and human expertise in operational river forecasting

Pagano, Thomas C. (författare)
Bur Meteorol, Melbourne, Vic, Australia.
Pappenberger, Florian (författare)
European Ctr Medium Range Weather Forecast, Reading, Berks, England.
Wood, Andrew W. (författare)
Natl Ctr Atmospher Res, Boulder, CO 80307 USA.
visa fler...
Ramos, Maria-Helena (författare)
Inst Natl Rech Sci & Technol Environm & Agr IRSTE, Aix En Provence, France.
Persson, Anders (författare)
Uppsala universitet,Luft-, vatten- och landskapslära
Anderson, Brett (författare)
Bur Meteorol, Melbourne, Vic, Australia.
visa färre...
Bur Meteorol, Melbourne, Vic, Australia European Ctr Medium Range Weather Forecast, Reading, Berks, England. (creator_code:org_t)
2016-06-21
2016
Engelska.
Ingår i: WILEY INTERDISCIPLINARY REVIEWS-WATER. - : Wiley. - 2049-1948. ; 3:5, s. 692-705
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)
NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy