• Energy Research

International audience

Abstract Urban expansion, driven by population and economic growth, has been a major contributor to increased levels of energy consumption across the globe. Beirut, Lebanon’s capital, is no exception in facing a surge in its demand for electricity as it expands. However, with frequent power outages, underpinning a largely problematic power sector, Beirut’s demand for electricity is becoming a real hurdle that impedes the city’s economic growth and development. This paper introduces a near-city-scale building energy model, BEirut Energy Model BEEM, which estimates the building stock’s electricity consumption in two different districts in Beirut. The methodology uses rule-based expert data for an archetypal classification of the buildings based on their functions and periods of construction with their corresponding attributes including the number of floors, number of apartments, and bimonthly electricity consumption to generate a 3D model for 3630 buildings coupled to the hourly weather conditions and topographic map, which is then simulated in EnergyPlus. The predicted consumption of 2311 buildings is then calibrated with actual available metered data, to adapt the model to Beirut’s occupancy and users’ behaviors. Calibrated results are mapped to reveal the spatiotemporal distribution of energy peak demands which provide insights for future interventions. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning.

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

International audience

International audience

Dataset contains the outputs of the AgriCarbon-EO Agronomical modeling tool for the carbon and water flux estimates by bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model. ----------------------- -for TILE : T31TCJ -for year: 2017 -for Winter wheat crops - at 10 m resolution Maps: -file: "GLA_statmap.tif" Description: A raster with 4 bands containing respectivly: *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The number of images that are assimilated into SAFYE-CO2 from 2016/11/01 until 2017/08/01 -file: "emerg_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016). *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion. -file: "LUEa_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ. *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ. -file: "SENa_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C. *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C. -file: "SENb_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of SENb retrieved by the SAFYE-CO2 inversion. *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion. -file: "PRT_La_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of DAM retrieved by the SAFYE-CO2 inversion. *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion. -file: "DAM_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2. *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion in g/m2. -file: "NEP_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2. *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2. Shapefiles a GIS: -file: "S2_TILE_T31TCJ.shp" Description: shape file of the contour of the T231 TCJ sentinel2 tile -file: "FR_AUR.shp" Description: shape file of the contour of AURADE experimental field -file: "FR_AUR_TOWER.shp" Description: shape file of the location of the AURADE eddy covariance flux tower -file: "POI_2017.shp" Description: shape file of the location of points of interest that illustrate the ... paper -file: "ESU_DAM.shp" Description: shape file of the contour of the plots where dry biomass samples where taken. -file: "ESU_DAM_points.shp" Description: shape file of the location of the points where dry biomass samples where taken. -file: "mapT31TCJ_spamaps.qgz" QGIS project file for the visualisation of the NEP maps.

In this paper we present a study on the use of remote sensing data combined to the 3D modeling of radiative transfer (RT) and energy balance in urban canopies in the aim to improve our knowledge on anthropogenic heat fluxes in several European cities (London, Basel, Heraklion, and Toulouse). The approach is based on the forcing by the use of LandSAT8 data of a coupled radiative transfer model DART (Direct Anisotropic Radiative Transfer) (www.cesbio.upstlse.fr/dart) with an energy balance module. LandSAT8 visible remote sensing data is used to better parametrize the albedo of the urban canopy and thermal remote sensing data is used to enhance the anthropogenic component in the coupled model. This work is conducted in the frame of the H2020 project URBANFLUXES, which aim is to improve the efficiency of remote-sensing data usage for the determination of the anthropogenic heat fluxes in urban canopies [5].

As a major hotspot of climate change, Lebanon suffers from a water resources crisis enhanced by the increase of anthropogenic activities. In this paper, the impacts of climate change and of the Syrian refugee crisis are combined with the impact of demographic growth to assess their aggregated impact on seawater intrusion in the Tripoli aquifer. A hydrogeological model is used to assess the seawater intrusion evolution for the next 25 years with respect to three phenomena: seawater rise, variation of incoming freshwater flux, and the change of the extraction rate of the pumping wells. Our study shows that the freshwater/seawater interface will move forward inland about 103 m in the next 25 years, leading to the salinization of the aquifer at the position of the pumping wells. Only about 1% of the advancement of the interface is associated with seawater rise; the remaining contributions are 79% from climate change and 20% from demographic growth. Adding the impact of migration reduces the contribution of climate change from 79% to 52%. The results suggest that the remediation solutions and recommendations should take into account the long-term impacts of climate change and the impact of population migration.