Éditeur(s) : HAL CCSD Institute of Physics: Open Access Journals
Résumé : International audience
The Amazon river basin has been recently affected by extreme climatic events, such as the exceptional drought of 2005, with significant impacts on human activities and ecosystems. In spite of the importance of monitoring freshwater stored and moving in such large river basins, only scarce measurements of river stages and discharges are available and the signatures of extreme drought conditions on surface freshwater dynamics at the basin scale are still poorly known. Here we use continuous multisatellite observations of inundation extent and water levels between 2003 and 2007 to monitor monthly variations of surface water storage at the basin scale. During the 2005 drought, the amount of water stored in the river and floodplains of the Amazon basin was ~130 km3 (~70%) below its 2003-7 average. This represents almost a half of the anomaly of minimum terrestrial water stored in the basin as estimated using the Gravity Recovery and Climate Experiment (GRACE) data.
Environmental Research Letters

hal-00779275
https://hal.archives-ouvertes.fr/hal-00779275
https://hal.archives-ouvertes.fr/hal-00779275/document
DOI : 10.1088/1748-9326/7/4/044010

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.
ISSN: 2072-4292

Droits : http://hal.archives-ouvertes.fr/licences/copyright/
hal-01257912
https://hal.archives-ouvertes.fr/hal-01257912
https://hal.archives-ouvertes.fr/hal-01257912/document
https://hal.archives-ouvertes.fr/hal-01257912/file/remotesensing-07-15831.pdf
DOI : 10.3390/rs71215831

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control. Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil
ISSN: 2072-4292

hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255/document
https://hal.archives-ouvertes.fr/hal-01355255/file/010066865.pdf
IRD : fdi:010066865

Éditeur(s) : HAL CCSD
Résumé : International audience
The Heliosat-II method was developed to process Meteosat satellite images in order to assess the solar irradiation at surface. It is modified for its use with GOES satellite images in order to estimate the solar irradiation over French Guiana.Modifications include a change in the calibration formula, in the calculation of the cloud albedo, and in the values of the Linke turbidity factor. These modifications allow the improvement of the performances of the Heliosat-II model in Intertropical zones in French Guiana. The outcomes were compared to coincident measurements of hourly irradiation performed by ground stations.The results show that the use of the maximum apparent albedo as the cloud albedo (ρcmax), along with a fixed value of the Linke turbidity factor produces the best results; the bias was less than 23.61 Wh/m2 (5% of the mean value of the measurements) overall, and the RMSE was less than 109.39 W/m2 (24%) overall when using hourly values. In the case of daily means, the RMSE drops to less than 10.08 W/m2 (10%) overall, whereas the overall bias remains unchanged, as expected. The model performs better in clear skies than in cloudy skies. The results suggest that the Linke turbidity factor used in the Heliosat-II method does not reproduce well the attenuation of the solar radiation in French Guiana. The results were compared to those obtained from Heliosat II method and Meteosat images (HelioClim-3 database) and we found a better accuracy for GOES satellite data, which exhibits smaller viewing angle. The use of the Heliosat II method with GOES images allows assessment of the SSI with a good accuracy and high temporal and spatial resolutions.
Canadian Journal of Remote Sensing

meteo-01304576
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304576
DOI : 10.1080/07038992.2015.1040876

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.
ISSN: 2072-4292

meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/document
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/file/remotesensing-07-15831.pdf
DOI : 10.3390/rs71215831