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Assimilation of Streamflow Observations

  • Seong Jin NohEmail author
  • Albrecht H. Weerts
  • Oldrich Rakovec
  • Haksu Lee
  • Dong-Jun Seo
Reference work entry

Abstract

Streamflow is arguably the most important predictor in operational hydrologic forecasting and water resources management. Assimilation of streamflow observations into hydrologic models has received growing attention in recent decades as a cost-effective means to improve prediction accuracy. Whereas the methods used for streamflow data assimilation (DA) originated and were popularized in atmospheric and ocean sciences, the nature of streamflow DA is significantly different from that of atmospheric or oceanic DA. Compared to the atmospheric processes modeled in weather forecasting, the hydrologic processes for surface and groundwater flow operate over a much wider range of time scales. Also, most hydrologic systems are severely under-observed. The purpose of this chapter is to provide a review on streamflow measurements and associated uncertainty and to share the latest advances, experiences gained, and science issues and challenges in streamflow DA. Toward this end, we discuss the following aspects of streamflow observations and assimilation methods: (1) measurement methods and uncertainty of streamflow observations, (2) streamflow assimilation applications, and (3) benefits and challenges streamflow DA with regard to large-scale DA, multi-data assimilation, and dealing with timing errors.

Keywords

Streamflow Observation Data assimilation Hydrologic modeling Ensemble Kalman filtering Particle filtering Variational assimilation Multiscale bias correction Maximum likelihood ensemble filtering 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Seong Jin Noh
    • 1
    Email author
  • Albrecht H. Weerts
    • 2
    • 3
  • Oldrich Rakovec
    • 4
    • 6
  • Haksu Lee
    • 7
  • Dong-Jun Seo
    • 5
  1. 1.Department of Civil EngineeringThe University of Texas at ArlingtonArlingtonUSA
  2. 2.Operational Water ManagementInland Water Systems, DeltaresDelftThe Netherlands
  3. 3.Hydrology and Quantative Water Management GroupWageningen University and ResearchWageningenThe Netherlands
  4. 4.Helmholtz Centre for Environmental Research – UFZLeipzigGermany
  5. 5.Department of Civil EngineeringThe University of Texas at ArlingtonArlingtonUSA
  6. 6.Faculty of Environmental SciencesCzech University of Life SciencesPragueCzech Republic
  7. 7.National Oceanic and Atmospheric AdministrationSilver SpringUSA

Section editors and affiliations

  • Hamid Moradkhani
    • 1
  • Albrecht Weerts
    • 2
  1. 1.Department of Civil & Environmental EngineeringPortland State UniversityPortlandUSA
  2. 2.Inland Water SystemsDeltaresThe Netherlands

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