Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution

Giulia Ercolani; Fabio Castelli

doi:10.29007/39wq

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Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution

8 pages•Published: September 20, 2018

Giulia Ercolani and Fabio Castelli

Abstract

A mixed variational-Monte Carlo scheme is employed to assimilate streamflow data at multiple locations in a distributed hydrologic model for flood forecasting purposes. The goal of this work is to assess the role of the spatial distribution of the assimilation points in terms of forecasts accuracy. The area of study is Arno river basin, and the strategy of investigation is to focus on one single nearly-flood event, performing various assimilation experiments that differ only in number and location of the assimilation sites.

Keyphrases: data assimilation, Distributed hydrologic model, Flood forecasting, Observations spatial distribution

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 668--675

Links:	https://easychair.org/publications/paper/jQlw
	https://doi.org/10.29007/39wq

BibTeX entry

@inproceedings{HIC2018:Mixed_Variational_Monte_Carlo_Assimilation,
  author    = {Giulia Ercolani and Fabio Castelli},
  title     = {Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {668--675},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/jQlw},
  doi       = {10.29007/39wq}}

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