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Potential of remote sensing and open street data for flood mapping in poorly gauged areas: a case study in Gonaives, Haiti

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Abstract

The Hispaniola Island, in the Caribbean tropical zone, is prone to extreme flood events. Floods are caused by tropical springs and hurricanes and may lead to human losses, economical damages, and spreading of waterborne diseases. Flood studies based upon hydrological and hydraulic modelling are hampered by almost complete lack of hydro-meteorological data. Thenceforth, and given the cost and complexity in the organization of field measurement campaigns, the need for exploitation of remote sensing data, and open source data bases. We present here a feasibility study to explore the potential of (i) high-resolution of digital elevation models (DEMs) from remote imagery and (ii) remotely sensed precipitation data, to feed hydrological flow routing and hydraulic flood modelling, applied to the case study of river La Quinte closed to Gonaives (585 km2), Haiti. We studied one recent flood episode, namely hurricane Ike in 2008, when flood maps from remote sensing were available for validation. The atmospheric input given by hourly rainfall was taken from downscaled Tropical Rainfall Measuring Mission (TRMM) daily estimates, and subsequently fed to a semi-distributed DEM-based hydrological model, providing an hourly flood hydrograph. Then, flood modelling using Hydrologic Engineering Center River Analysis System (HEC-RAS 1D, one-dimensional model for unsteady open channel flow) was carried out under different scenarios of available digital elevation models. The DEMs were generated using optical remote sensing satellite WorldView-1 and Shuttle Radar Topography Mission (SRTM), combined with information from an open source database (OpenStreetMap). Observed flood extent and land use have been extracted using Système Pour l’Observation de la Terre-4 (SPOT-4) imagery. The hydraulic model was tuned for floodplain friction against the observed flooded area. We compared different scenarios of flood simulation and the predictive power given by model tuning. Our study provides acceptable results in depicting flooded areas, especially considering the tremendous lack of ground data, and shows the potential of hydrological modelling approach fed by remote sensing information in Haiti, and in similarly data-scarce areas. Our approach may be useful to provide depiction of flooded areas for the purpose of (i) flood design for urban planning under a frequency-driven approach and (ii) forecasting of flooded areas for warning procedures, pending availability of weather forecast with proper lead time.

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Acknowledgments

The presented work took place during Eng. Bozza’s internship and was carried out in cooperation between SERTIT institute (University of Strasbourg) and DICA Dept., Hydrology Division (Politecnico di Milano). The authors wish to acknowledge KAL-Haiti project and International Charter Space and Major Disaster. The KAL-Haiti project is funded by the French Agence Nationale de la Recherche (ANR) under grant number 2010 HAIT 008 01. Contributions of data providers to the database, whose list is available on the Kal-Haiti website, are acknowledged by the KAL-Haiti project team. Dr. Gabriele Confortola acknowledges contribution from “I-CARE” project, funded under the 5 % scheme from Politecnico di Milano, year 2009. Two anonymous reviewers are kindly acknowledged for their useful comments.

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Authors and Affiliations

  1. Hydrology Division, Department of ICA, Politecnico di Milano, 32 Leonardo da Vinci Sq, 20133, Milano, Italy

    A. Bozza, G. Confortola, A. Soncini & D. Bocchiola

  2. SERTIT, University of Strasbourg, Pole API - Parc d’innovation, 300 Boulevard Sébastien Brant, BP 10413, 67412, Illkirch Graffenstaden, France

    A. Bozza, A. Durand & B. Allenbach

  3. EVK2CNR Association, San Bernardino 145, 24126, Bergamo, Italy

    D. Bocchiola

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  1. A. Bozza
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  2. A. Durand
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  3. G. Confortola
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  4. A. Soncini
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  5. B. Allenbach
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  6. D. Bocchiola
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Correspondence to D. Bocchiola.

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Bozza, A., Durand, A., Confortola, G. et al. Potential of remote sensing and open street data for flood mapping in poorly gauged areas: a case study in Gonaives, Haiti. Appl Geomat 8, 117–131 (2016). https://doi.org/10.1007/s12518-016-0171-x

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  • DOI: https://doi.org/10.1007/s12518-016-0171-x

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