Assimilation of Streamflow Observations

Seong Jin Noh, Albrecht H. Weerts, Oldrich Rakovec, Haksu Lee, Dong-Jun Seo

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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.
Original languageEnglish
Title of host publicationHandbook of Hydrometeorological Ensemble Forecasting
EditorsQingyun Duan, Florian Pappenberger, Jutta Thielen, Andy Wood, Hannah L. Cloke, John C. Schaake
PublisherSpringer Verlag
Pages1-36
ISBN (Electronic)9783642404573
ISBN (Print)9783642404573
DOIs
Publication statusPublished - 1 Jun 2018

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