Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate

Domaine:

climategeospatial

Type de record:

paper
Créateur:
Lemenkova, Polina
Éditeur:
Uni
Éditeur:
CCSDMDPI
Hôte:avatar
International audience This articles presents a new series of maps showing the climate and environmental variability of Botswana. Situated in southern Africa, Botswana has an arid to semi-arid climate, which significantly varies in its different regions: Kalahari Desert, Makgadikgadi Pan and Okavango Delta. While desert regions are prone to droughts and periods of extreme heat during the summer months, other regions experience heavy downpours, as well as episodic and unpredictable rains that affect agricultural activities. Such climatic variations affect social and economic aspects of life in Botswana. This study aimed to visualise the non-linear correlations between the topography and climate setting at the country’s scale. Variables included T °C min, T °C max, precipitation, soil moisture, evapotranspiration (PET and AET), downward surface shortwave radiation, vapour pressure and vapour pressure deficit (VPD), wind speed and Palmer Drought Severity Index (PDSI). The dataset was taken from the TerraClimate source and GEBCO for topographic mapping. The mapping approach included the use of Generic Mapping Tools (GMT), a console-based scripting toolset, which enables the use of a scripting method of automated mapping. Several GMT modules were used to derive a set of climate parameters for Botswana. The data were supplemented with the adjusted cartographic elements and inspected by the Geospatial Data Abstraction Library (GDAL). The PDSI in Botswana in 2018 shows stepwise variation with seven areas of drought: (1) −3.7 to −2.2. (extreme); (2) −2.2 to −0.8 (strong, southern Kalahari); (3) −0.8 to 0.7 (significant, central Kalahari; (4) 0.7 to 2.1 (moderate); (5) 2.1 to 3.5 (lesser); (6) 3.5 to 4.9 (low); (7) 4.9 to 6.4 (least). The VPD has a general trend towards the south-western region (Kalahari Desert, up to 3.3), while it is lower in the north-eastern region of Botswana (up to 1.4). Other values vary respectively, as demonstrated in the presented 12 maps of climate and environmental inventory in Botswana.

Visit

hal.science

Tags

AfricaprogrammingmappingGMTdroughtdata sciencecomputer scienceclimatecartographyBotswana+18

Licenses

http://creativecommons.org/licenses/by/info:eu-repo/semantics/OpenAccess

Similaires

Environmental mapping of Burkina Faso using TerraClimate data and satellite images by GMT and R scriptsMapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR dataUsing advanced microwave scanning radiometer (AMSR-E) data for soil moisture mapping over Botswana.Developing gridded climate data using neural networks: high-resolution historical climate and future projections for Africa Developing gridded climate data for Africa with deep neural networksCountry-Scale Mapping Of Forest Parameters Using Deep Learning And Tandem-X Insar DataBias-correction of Surface Wind over Reunion and Mauritius Islands using CORDEX Regional Climate Model: RegCM4.7

Environmental mapping of Burkina Faso using TerraClimate data and satellite images by GMT and R scripts

In this paper, the climate and environmental datasets were processed by the scripts of Gene

Mapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR data

International audience The potentialities of ENVISAT ASAR (Advanced Synthetic Apertur

Using advanced microwave scanning radiometer (AMSR-E) data for soil moisture mapping over Botswana.

Developing gridded climate data using neural networks: high-resolution historical climate and future projections for Africa Developing gridded climate data for Africa with deep neural networks

Databases of high-resolution interpolated climate data are essential for climate change research, su

Country-Scale Mapping Of Forest Parameters Using Deep Learning And Tandem-X Insar Data

International audience Highly accurate estimates of canopy height (CH) and above grou

Bias-correction of Surface Wind over Reunion and Mauritius Islands using CORDEX Regional Climate Model: RegCM4.7

International audience The world needs energy for its social and economic development