Mapping environmental and climate variations by GMT: a case of Zambia, Central Africa

Zambia recently experienced several environmental threats from climate change such as droughts, temperature rise and occasional flooding and they all affect agricultural sustainability and people well-being through negative effects on plants and growing crops. This paper is aimed at showing variations in several climate and environmental parameters in Zambia showing spatial variability and trends in different regions of Zambia's key environmental areas (Zambezi River and tributaries), Livingstone near the Victoria Falls and central region with Muchinga Mountains. A series of 10 maps was plotted using data from TerraClimate dataset: precipitation, soil moisture, Palmer Drought Severity Index (PDSI), downward surface shortwave radiation, vapor pressure deficit and anomalies, potential and actual evapotranspiration and wind speed with relation to the topographic distribution of elevations in Zambia plotted using GEBCO/SRTM data. The data range of the PDSI according to the index values ranged from minimum at -5.7 to the maximum at 16.6 and mean at 7.169, with standard deviation at 4.278. The PDSI is effective in quantifying drought in long-term period. Because PDSI index applies temperature data and water balance model, it indicates the effect of climate warming on drought by correlation with potential evapotranspiration. The maximum values for soil moisture of Zambia show minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m. This study is technically based on using the Generic Mapping Tools (GMT) as cartographic scripting toolset. The paper contributes to the environmental monitoring of Zambia by presenting a series of climate and environmental maps that are beneficial for agricultural mapping of Zambia.


Background
The geography of Zambia ( Fig. 1) is characterized by its location in central-southern Africa with a tropical climate and developed river network. Hydrologically, Zambia hosts the two major river basins: geological exploration in the north of Zambia that can deteriorate the quality of soils and cause pollution of rivers (Banda and Sichilongo, 2006).
Floods are becoming frequent and severe in selected regions of Zambia. This requires developing adaptation strategies for people exposed to flooding disasters (Mabuku et al. 2019). In view of this, visualizing environmental and climate variables through mapping helps to analyze and predict flood duration and dimensions of land affected by floods to determine how rural household can adapt to flooding hazards. On the other hand, rainfall, temperature and water level have direct effects of on fisheries ecosystems and people who derive livelihoods from fish yield (Ng'onga et al. 2019). Variations in climate and environmental setting have social consequences affecting people.

Study aim and objective
The aim of the present study is to visualize, explain and analyze spatial variations of several environmental and climate parameters over Zambia by employing Generic Mapping Tools (GMT) developed by Wessel et al. (2019) and freely available online (https://www.generic-mapping-tools.org/) to distinguish the regions of the country most affected by the climate and environmental effects. To achieve this aim, the objective of this study was to map a series of the environmental and climate datasets from TerraClimate (Abatzoglou et al. 2018) in the area of Zambia, central-southern Africa.
Technical goal was to demonstrate the effectiveness of the usage of scripting technical in cartographic data processing applying multi-source data to the agricultural and environmental studies.
The novelty and practical importance of this study consists in a multi-disciplinary approach which applied advanced cartographic method of scripting mapping by GMT for environmental studies of Zambia that has not been presented so far in the existing literature. Practical purpose is to present new interpretations of the climate variability for the area of Zambia using TerraClimate dataset for 2018.

Data
Main data source used in this study include the TerraClimate high-resolution (1/24°, i.e. approximately 4-km) global dataset that presents monthly data in raster format showing climate and climatic water balance for global terrestrial surfaces (Abatzoglou et al. 2018). The selected files in netCDF format have been produced for each year presenting a variable combination of the monthly data in the TerraClimate dataset. The topographic map in Figure 1 has been plotted using GEBCO grid of the Earth's relief (Schenke, 2016), largely used in geosciences and topographic mapping (Lemenkova, 2020d. This study is based on the methodology of GMT developed by Wessel et al. (2019)

Methods
The approach of GMT is based on a modular cartographic approach with special parameters defined and used for plotting each cartographic element, which makes it easy to add new layers on the raster map (Lemenkova 2020c). Compared to the mainstream GIS approaches based on graphic user interface (GUI) or other approaches in Earth science (e.g. Klaučo et al. 2014, 2017, Lemenkov and Lemenkova, 2021bSuetova et al. 2005aSuetova et al. , 2005bLemenkova, 2011;Lemenkova et al. 2012), the GMT can be considered as a generalization of the programming approach with respect to the cartography where geospatial data are processed using scripting techniques.

Results and Discussion
This study implemented the environmental and climate modelling of the raster data from the opensource TerraClimate dataset and processed using GMT. For compatibility reasons, the maps are The GMT scripts have been run for each map from the presented series, therefore this approach leaves many options to the application in further similar studies of the environmental models. Figure 1 shows a topographic map of Zambia with visualized variations in heights in the study area. The elevation range has been inspected by Geospatial Data Abstraction Library (GDAL) using 'gdalinfo' utility for each map, to check up the statistical data. According to GDAL, the topography over the study area varies from minimum at 106 m to a maximum at 2846 m, with a mean at 1073.898 m, and standard deviation at 268.713 m.   Figure 3 shows the mapping results for a soil moisture in Zambia. The soil moisture is driven with a precipitation, temperature and properties of soil (such as permeability, porosity, density of soils) that enforces a correspondence between climate, geologic and environmental variables and in turn affects vegetation. As output the map obtains the maximum values for the soil moisture of Zambia as follows: minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m.       can be illustrated by its structure that points at the relationship between water and energy in the cycles of soil, land surface and atmosphere over Zambia.