Copyright 2020 - copyright UMR ESPACE-DEV - 2017

Nos dernières publications - Mars 2020

icone documentation publicationAlexandre Cyprien, Johary R., Catry ThibaultMouquet Pascal, Revillion C., Rakotondraompiana S., Pennober G. A Sentinel-1 based processing chain for detection of cyclonic flood impacts. Remote Sensing, 2020, 12 (2), p. art. 252 [18 p.] .

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

hurricane ; cyclone ; flood ; Sentinel 1 time series ; change detection ; NDR ; SAR

Affiliation IRD : UMR 228 (ESPACE-DEV)

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Satge FrédéricDefrance DimitriSultan BenjaminBonnet Marie-PauleSeyler FrédériqueRouché Nathalie, Pierron F., Paturel Jean-Emmanuel. Evaluation of 23 gridded precipitation datasets across West Africa. Journal of Hydrology, 2020, 581, p. art. 124412 [19 p.].

This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000-2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling-Gupta Efficiency (KGE) changes at the local scale and the P-dataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly time-step, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (Le day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.

Precipitation datasets ; Reliability ; West Africa

Affiliation IRD : UMR 228 (ESPACE-DEV) ; UMR 050 (HSM)

Descr. géo. : AFRIQUE DE L'OUEST

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