The ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in the South of Reunion Island, in Saint Joseph.

Le projet a pour objectif d'améliorer le signal de la pluie détectée par les géophones en comparant les données météorologiques d'un disdromètre, d'un pluviomètre et de 3 géophones afin d'extraire des données des sismographes pour mieux comprendre le transport sédimentaire issu du réseau sismologique installé dans la rivière des pluies et la rivière du Mat. Les objectifs sont : 1) déterminer les caractéristiques sismiques de la pluie sur le site de mesures 2) A terme, comprendre le déclenchement des éboulements et glissements liés aux pluies A court terme, ce projet devrait aussi permettre de : 1) comprendre pour un même type de pluie l'influence de sols de rugosités différentes sur les signaux enregistrés par les sismomètres 2) intégrer/contraindre pour un même type de sol l'influence de types de pluies différentes sur les enregistrements des sismomètres 3) déterminuer l'influence des tailles des gouttes et du nombre de gouttes (indications données par le disdromètre) sur le signal sismique

The overall objective of the ESPOIRS project is to obtain a better understanding of the variability, statistical properties and formation mechanisms of intense tropical precipitation at regional and local scales. ESPOIRS is thus interested in the entire life cycle of precipitation at several space-time scales. * Through the analysis of the distribution of the large-scale humidity field which drives the formation of precipitation at the regional scale using a GNSS network. * Through the characterization of internal (dynamics, microphysics) and external (interactions with the relief) processes, which drive the formation and life cycle of extreme weather events at the local scale => transportable Polarized Doppler X-band precipitation radar.

WW3 model reanalysis on SWIO (south-western indian ocean) area at 0.5 degree of resolution

The resource provides two land cover maps of Réunion Island for the years 1950 and 2022 derived from the analysis of ortho-photographs at the island scale (Source IGN). The produced typology uses five cover classes: forest, low vegetation, agriculture, urban, and shadow (related to topography). The method used is based on encoding the two aligned rasters, converted into a single band of grayscale for 2022, using a vision-transformer deep learning model. From the features calculated for each pixel, a random forest classification model is trained separately for each year using a set of ROIs (Regions Of Interest), target polygons delineated within each of the selected classes through photo-interpretation of the original images. Model validation is performed on independent sets of polygons also defined by photo-interpretation. The maps provided in the resource are derived from the prediction of cover classes for both years using the trained and validated models. These are raw predictions, meaning that no post-processing has been applied to reduce potential noise due to classification errors. The shared resource is part of the results from the FRAG'ILE research and development program (FRAGmentation en milieu InsuLairRE, UR / CBNM/ IRD, funded by OFB, https://fragile.frama.io).

Elevational Transect on the East side of the Piton des Neiges. Implementation of small permanents plots every 200 m of height between 350 m and 2950 m. Differents biotics and abiotics parameters are observed on plots: - Climatic variables (T, RH) - Physico-chemical analyses of grounds - Vegetation and arthropods inventories...

This dataset encompasses model outputs generated by the Weather Research and Forecasting (WRF) regional climate model. A high-resolution (~1km) downscaling simulation was performed over two tropical islands, Reunion and Mauritius, situated in the South-West Indian Ocean (SWIO), with initial and boundary conditions provided by the ERA5 reanalysis with a global resolution of 0.25° × 0.25°. The simulation used three nested domains sequentially configured with spatial resolutions of 9, 3, and 1km, respectively, with a downscaling ratio of 3. The physical configurations of this simulation were determined through previous modeling studies and sensitivity tests. The published simulation data currently covers a period of 10 years, starting from 1991 (with the possibility to be extended to 30 years). Over 60 output variables were selected for publication with open access, including those related to the intermittent energy resources (e.g., surface solar radiation and its direct/diffuse components, wind speed/direction at multiple vertical levels, and precipitation, of interest for the run-off-river hydropower), as well as the widely used climatic/meteorological variables (e.g., temperature, pressure, humidity, etc.) at a temporal resolution varying from a day up to 30 minutes. All the data are available through an open-access data server, where an intelligent algorithm is applied to simplify the download process for data users. For the first time, a long-term, high-resolution climate/meteorological dataset covering Reunion and Mauritius has been simulated and published as open-access data, yielding substantial benefits to studies on climate modeling, weather forecasting, and even those related to climate change in the SWIO region. In particular, this dataset will enable a better understanding of the temporal and spatial characteristics of intermittent climate-related energy resources, consequently facilitating their implementation towards a green and low-carbon future.

The ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in the north of Mahé Island, in Seychelles.

The ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in Toamasina (Tamatave) in Madagascar.