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Handbook of Hydrometeorological Ensemble Forecasting pp 1–36Cite as

Assimilation of Streamflow Observations

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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.

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Authors and Affiliations

  1. Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX, USA

    Seong Jin Noh

  2. Operational Water Management, Inland Water Systems, Deltares, Delft, The Netherlands

    Albrecht H. Weerts

  3. Hydrology and Quantative Water Management Group, Wageningen University & Research, Wageningen, The Netherlands

    Albrecht H. Weerts

  4. Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

    Oldrich Rakovec

  5. Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX, USA

    Dong-Jun Seo

  6. Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic

    Oldrich Rakovec

  7. National Oceanic and Atmospheric Administration, Silver Spring, MD, USA

    Haksu Lee

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  1. Seong Jin Noh
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  2. Albrecht H. Weerts
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  3. Oldrich Rakovec
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  4. Haksu Lee
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Correspondence to Seong Jin Noh .

Editor information

Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, USA

    John C. Schaake

Section Editor information

  1. Department of Civil & Environmental Engineering, Portland State University, Portland, Oregon, USA

    Hamid Moradkhani

  2. Inland Water Systems, Deltares, The Netherlands

    Albrecht Weerts Expert Hydrology & Forecasting

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Noh, S.J., Weerts, A.H., Rakovec, O., Lee, H., Seo, DJ. (2018). Assimilation of Streamflow Observations. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_33-2

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  1. Latest

    Assimilation of Streamflow Observations
    Published:
    01 June 2018

    DOI: https://doi.org/10.1007/978-3-642-40457-3_33-2

  2. Original

    Assimilation of Streamflow Observations
    Published:
    03 April 2018

    DOI: https://doi.org/10.1007/978-3-642-40457-3_33-1

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