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Volume 16, issue 10 | Copyright

Special issue: Latest advances and developments in data assimilation for...

Hydrol. Earth Syst. Sci., 16, 3863-3887, 2012
https://doi.org/10.5194/hess-16-3863-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Oct 2012

Research article | 29 Oct 2012

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Y. Liu1,2, A. H. Weerts3, M. Clark4, H.-J. Hendricks Franssen5, S. Kumar2,6, H. Moradkhani7, D.-J. Seo8, D. Schwanenberg3,9, P. Smith10, A. I. J. M. van Dijk11, N. van Velzen12,13, M. He14,15, H. Lee14,16, S. J. Noh17,18, O. Rakovec19, and P. Restrepo20 Y. Liu et al.
  • 1Earth System Science Interdisciplinary Center, the University of Maryland, College Park, MD, USA
  • 2Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Deltares, Delft, The Netherlands
  • 4Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 5Agrosphere (IBG 3), Forschungszentrum Jülich, Jülich, Germany
  • 6Science Applications International Corporation, Beltsville, MD, USA
  • 7Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
  • 8Department of Civil Engineering, University of Texas at Arlington, Arlington, TX, USA
  • 9Institute of Hydraulic Engineering and Water Resources Management, University of Duisburg-Essen, Duisburg, Germany
  • 10Lancaster Environment Centre, Lancaster University, Lancaster, UK
  • 11Fenner School for Environment and Society, Australian National University/CSIRO Land and Water, Canberra, Australia
  • 12Delft Technology University, Delft, The Netherlands
  • 13VORtech, Delft, The Netherlands
  • 14Office of Hydrologic Development, National Weather Service, Silver Spring, MD, USA
  • 15Riverside Technology, Inc., Fort Collins, CO, USA
  • 16University Corporation for Atmospheric Research, Boulder, CO, USA
  • 17Department of Urban and Environmental Engineering, Kyoto University, Kyoto, Japan
  • 18Water Resources and Environment Research Department, Korea Institute of Construction Technology, Goyang Si Gyeonggi Do, Korea
  • 19Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, The Netherlands
  • 20North Central River Forecast Center, National Weather Service, Chanhassen, MN, USA

Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters.

The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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