# Evaluating the Performance of Palmer Drought Severity Index (PDSI) in Various Vegetation Regions of the Ethiopian Highlands

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Evaluating the Performance of Palmer Drought Severity Index (PDSI) in Various Vegetation Regions of the Ethiopian Highlands. Acta Biologica Marisiensis, 2021, 4 (2), pp.14-31. 10.2478/abmj-2021-0010. hal-03498716

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# EVALUATING THE PERFORMANCE OF PALMER DROUGHT SEVERITY INDEX (PDSI) IN VARIOUS VEGETATION REGIONS OF THE ETHIOPIAN HIGHLANDS

Polina LEMENKOVA¹*

¹Laboratory of Image Synthesis and Analysis, École polytechnique de Bruxelles (Brussels Faculty of Engineering), Université Libre de Bruxelles (ULB). Brussels, Belgium

*Correspondence:

Polina LEMENKOVA

polina.lemenkova@ulb.be or pauline.lemenkova@gmail.com

**Received:** 2 November 2021; **Accepted:** 5 December 2021; **Published:** 30 December 2021

**Abstract:** This paper focuses on the environment of Ethiopia, a country highly sensitive to droughts severely affecting vegetation. Vegetation monitoring of Ethiopian Highlands requires visualization of environmental parameters to assess droughts negatively influencing agricultural sustainable management of crops. Therefore, this study presented mapping of several climate and environmental variables including Palmer Drought Severity Index (PDSI). The data were visualized and interpreted alongside the topographic data to evaluate the environmental conditions for vegetation. The datasets included WorldClim and GEBCO and Digital Chart of the World (DCW). Research has threefold objectives: i) environmental mapping; ii) technical cartographic scripting; iii) data processing. Following variables were visualized on seven new maps: 1) topography; 2) soil moisture; 3) T °C minimum; 4) T °C maximum; 5) Wind speed; 6) Precipitation; 7) Palmer Drought Severity Index (PDSI). New high-resolution thematic environmental maps are presented and the utility of GMT for mapping multi-source datasets is described. With varying degrees of soil moisture (mean value of 15.0), min T°C (-1.8°C to 24°C), max T°C (14.4°C to 40.2°C) and wind speed (0.1 to 6.1 m/s), the maps demonstrate the variability of the PDSI fields over the country area (from -11.7 to 2.3) induced by the complex sum of these variables and intensified by the topographic effects notable over the Ethiopian Highlands which can be used for vegetation analysis. The paper presents seven new maps and contributes to the environmental studies of Ethiopia.

**Keywords:** cartography; vegetation; drought; PDSI; precipitation; soil moisture; temperature.

## 1. Introduction

Drought, severely affecting vegetation, is caused by the shortage in water supply. It has twofold factors: atmospheric origin (precipitation), and soil moisture (surface water). There are many controlling factors which determine the distribution of drought, including atmospheric (precipitation, temperature, intensity and direction of winds), geological (soil moisture, permeability of subsoils), and hydrological (dense of river

network and intensity of streams). Visualizing spatial patterns of droughts is crucial for the environmental monitoring of the region with such contrasting climate setting as Ethiopia. Although the duration drought may vary from several days to months or years, its environmental, agricultural and social consequences may have a substantial impact on both nature ecosystems and human existence through the affected agricultural regional
economy. Ethiopia is a country with sensitive climate environmental setting, contrasting topography, unique geologic parameters (location of the Afar Triple Junction and distribution of the part of the East African Rift System and Great Rift Valley), **Fig. 1**. The complicated combination of the climatic and topographic factors results in notable drought disasters, recorded in the Somali, the Afar Depression (geological region of the Afar Triple Junction), deserts and lowland regions as the area most affected by droughts. With a focus on visualizing the drivers of drought in Ethiopia, this paper presents a series of maps on current environmental-climate setting of Ethiopia with aim to demonstrate why drought is distributed in a certain correlation with topographic relief, a question which is of interest to the environmental monitoring in

Ethiopia. Since humans are dependent upon climate setting, environmental conditions and ecosystems for agricultural production and services, regular updated climate monitoring is essential not only for the physical geographic but also for the social studies, which explains and justifies the actuality of the undertaken research.

Agricultural problems caused by drought include a variety of processes. The most significant agricultural consequences include crop failure (Edossa et al., 2010; Suryabhagavan, 2017), soil and pasture losses (Tora et al., 2021; Mihretie et al., 2021) in vulnerable regions of Ethiopia. The direct economic and social issues include famine and diseases (Gebre et al., 2021), physical and mental health strain and children's health (Bahru et al., 2019; Dimitrova, 2021).

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Map of the study area. Mapping: GMT. Data: GEBCO, DCW. Source: author</figcaption></figure>
Furthermore, drought leads to the increased costs of food for consumers in economic sector and decrease in cattle population (Aragie and Thurlow, 2022). The indirect yet important consequence of drought includes broken supply chains in industry and production: reduced supplies to food processors, demand for fertilizer and farm labor.

The environmental consequences of drought include direct affect on the vulnerable species, such as fruits, flowers, vegetables, tree nuts, and medicinal herbs that are more sensitive to droughts compared to the field crops (Di Falco et al., 2010). Biodiversity consequences include a high risk for losses in species diversity of rare species if the water demand exceeds water supply (Legesse and Negash, 2021). The depletion of water in soils causes significant declines in vegetation productivity and growth of roots (Demelash et al., 2021) that finally affects biodiversity and ecosystems. This is caused by the deficit of water in surface and groundwater that declines during drought period, affecting water availability necessary for normal functionality of plants.

The aim of the present study is to plot a series of maps showing visualized computed index of droughts (Palmer Drought Severity Index, PDSI), and several supporting meteorological, topographic, climate and environmental data of Ethiopia using high-resolution datasets by technical tools of Generic Mapping Tools (GMT) cartographic scripting toolset. Besides environmental assessment and visualization, this paper discusses a GMT-based approach to prepare a series of meteorological maps of Ethiopia using a scripting approach. The presented maps visualize the following environmental variables in Ethiopia in 2018: 1) topography; 2) soil moisture; 3) T °C minimum; 4) T °C maximum; 5) Wind speed; 6) Precipitation; 7)

Palmer Drought Severity Index (PDSI). The presented paper serves the threefold research objectives:

i. Contributing to the environmental and climatic studies of Ethiopia through the visualized new seven thematic maps;

ii. Presenting new approaches of scripting tools for technical developing of the contemporary cartography;

iii. Utilizing multi-source datasets from the open repositories showing the application of the multi-source data in the environmental-climate research. The actuality of present study consists in the presented maps visualizing meteorological and climate parameters. Those are necessary for environmental monitoring of the country and assessment of climate change and its possible effects. Among other variables, this paper demonstrates the visualized map of the Palmer Drought Severity Index (PDSI), an index developed by Palmer in 1965 and widely used in various aspects of the environmental monitoring, e.g. as follows:

(a) to assess climate and environmental changes;

(b) to perform ecological monitoring;

(c) to complete climatic divisions for measurements of the hydrologic drought and to indicate severe or extreme drought using PDSI values (Alley, 1985).

The PDSI is the most widely used in climatology regional index of drought which enables to quantify intensity and period of droughts (start/end time) for the global long-term drought analysis (Alley, 1985). In computations of PDSI, we need to estimate the precipitation and temperature for the measurement of dryness (Dai et al., 2004).

Theoretically, the drought should be estimated based on the 'supply-and-demand' properties of soil moisture of the study area which includes such complex parameters as evapotranspiration reflecting the 'demand' of
soil, i.e. the need for water (Ficklin et al.,
2015).

The PDSI presents an approximated and
straightforward model of the drought
calculation based only on precipitation and
temperature. The PDSI is a standardized
measure, ranging from about -10 (extreme
drought) to +10 (extreme moisture) and 0 as
neutral conditions, of surface moisture setting
(Dai et al., 2004).

Practically, the comparative analysis of
these values allows to perform spatial analysis
of the variability of droughts over the study
area. The approaches to assess and calculate
drought in climate and environmental studies,
are diverse (Tesfamariam et al., 2019). More
sophisticated cases of the PDSI calculations
include the prognosis of possible drought based
on the existing data processed as time series
analysis using numerical modelling and
visualized graphs (Beyaztas et al., 2018).

Values, nature and spatial distribution of
the environmental parameters can be analyzed
using various techniques and approaches
through data processing, analysis, modelling
and visualization (Suetova et al., 2005;
Schenke and Lemenkova, 2008; Klaučo et al.,
2013; Jain et al., 2015; Lemenkov and
Lemenkova, 2021a, 2021b; Lemenkova, 2019c).
Climate data assessment methods use fieldwork
records from the local meteorological stations
where, for examples, the values of temperature
and their statistical extremes, wind speed and
direction, frequency and intensity of
precipitations are recorded directly. The
determined datasets are then stored as data
massifs in Climate Centers and Research Units
for further data processing (Asfaw et al., 2018).
On the other hand, the datasets in tabular
formats require detailed and effective mapping
for interpretation of the environmental
parameters.

Probably the most common approach of
cartographic visualization is based on the

ArcGIS (Gohl, 2006a, 2006b; Gauger et al., 2007; Lemenkova, 2011; Klaučo et al., 2017; Ghiglieri et al., 2020; Koroso et al., 2020; Kebede et al., 2021), which enables to perform various types of spatial data processing, modelling and visualization. Besides the commercial ArcGIS, the examples of the free open source GIS used for environmental mapping include ILWIS GIS, SAGA GIS and QGIS (Alemayehu et al., 2009; Lemenkova, 2020a, 2020c). While there have been cartographic attempts to present climate mapping of Ethiopia by the traditional GIS approaches, a scripting GMT-based approach enabling rapid data processing of Ethiopian ecosystems using machine learning methods has heretofore been lacking. Compared to the GIS applications, the GMT cartographic data processing obtained using scripting. Due to its console-based approach, the GMT is, to a certain extent, similar to the programming languages applied in geosciences (Lemenkova, 2019a, 2019b).

Retrieval of climate data from available
repositories for data processing is very
common. These may include government
records on climate and meteorological data,
field observations and questionnaire surveys
(Adgo et al., 2013), census data, crop yield data
from surveys (Eze et al., 2020), multi-temporal
images (Gebrehiwot et al., 2011). Mapping
meteorological variables provides a great
importance to analysis of climate change,
mitigation its environmental and social impacts
and local adaptation (Mera, 2018; Matewos,
2020).

Environmental monitoring, in turn, is
useful for evaluating global ecological and
social conditions within Ethiopia, a country
with extreme climate setting and sensitive
ecosystems (Haile et al., 2020). Therefore, the
application of the thematic GMT based mapping
method for monitoring droughts in Ethiopia, a
region with extreme temperatures, prone to
droughts, presents cartographic visualization of
the high-resolution data provided as a series of

The actuality of this study consists in the
added value on environmental data analysis
through advanced visualization which may
have direct benefits for policy makers and
studies on environmental risk assessment using
produced maps. Moreover, current research in
sustainable agriculture and food systems in
Ethiopia has a notable gap between the
environmental applications in farming and
technical methodologies of mapping and
cartographic visualization. At the same time,
these are necessary to process geospatial data
for possibilities of agricultural activities in
various regions of Ethiopia and crop vegetation
mapping.

The presented research aims to minimize
this gap by presenting a series of thematic maps
of Ethiopia prepared using advanced
methodologies of GMT for data processing in a
semi-automated regime for high-resolution
mapping. Environmental mapping of Ethiopia
using GMT presented observations of climate
variables including PDSI drought index to
detect and visualize variability in of climate
setting to assess their possible effects on
agriculture of Ethiopia. Using scripting
methods of GMT and TerraClimate data for
environmental mapping is ensured by the large
amounts of automation in GMT. The series of
the environmental maps may support data
analysis in sustainable farming of Ethiopia.
Therefore, this research can bring new data to
the environmental analysis and support
agriculture and public sectors on relevant topics
of environment and agriculture of Ethiopia.

## 2. Materials and methods

### Datasets

Data-driven mapping includes
visualization of the georeferenced datasets
which may vary in reliability, resolution and

base maps for developing further environmental
analysis using cartographic techniques.
source of origin. This necessarily rises a
question of the data quality control and
assessment. Publicly reknown open data
reputable repositories, such as GEBCO,
NOAA, USGS, GloVis (well-known sources
for the Landsat/Sentinel imagery), or
TerraClimate present the reliable source for the
input data suitable for mapping. This study
captures data from the GEBCO and
TerraClimate sources. Specifically, the datasets
used in this study utilize on the GEBCO
(GEBCO Compilation Group, 2020) used for
topographic mapping in **Fig. 1.** and
TerraClimate (Abatzoglou et al., 2018) used for
**Fig. 2–7.**

High-resolution (4 km) climate data from
the TerraClimate WorldClim (Fick and
Hijmans, 2017) CRUTS 4.0. sources have been
used to visualize and map series of climatic
parameters in Ethiopia on 2018: soil moisture;
extreme temperatures (T °C min and T °C
max), wind speed and precipitation and PDSI.
Data processing included five types of actions:

i. data search, selection and capture;

ii. data modeling and numerical
processing;

iii. data visualization, elimination and
symbolization using cartographic design tools;

iv. data behavior analysis: estimating
coherency and finding correlations in datasets
(min/max temperatures, soil moisture,
topographic elevations, wind speed, PDSI);

v. data interpretation and assessment:
explaining trends in spatial distribution of
categorical parameters and continuous fields.

Correct, up-to-date georeferenced data
about environmental and climate parameters
are essential for environmental monitoring of
such disasters as droughts, for supporting real-
time environmental hazards mapping and
climate control, and for performing correlation
analyses between geographic variables (for
instance, agricultural monitoring, crop mapping, assessment of wildlife habitat distribution). To aid in these efforts, TerraClimate published the set of climate data service to its online portal. The TerraClimate is a global repository of the climate variables, where each data in the NetCDF format contains an estimate of the climate measurements for various years. This study utilities the most recently available, that is 2018, as a case study.

The data from the TerraClimate repository present a useful resource as an input data source to terrestrial environmental mapping of Ethiopia. Clipped from the global original data for Ethiopia using the coordinates of the country borders, TerraClimate data produced a set of robust data layers on Ethiopia that has been adopted as the conceptual and input data

for the thematic series in the presented research: temperatures (min/max), soil moisture, wind speed and PDSI. The series of six climate grid from the TerraClimate source and a topographic grid based on GEBCO were handled by the GMT for mapping selected meteorological parameters over the country for 2018 and visualizing the drought-prone areas in Ethiopia. The presented georeferenced data of the climate variables in Ethiopia are visualized, with aim to highlight and compare patterns of data distribution over Ethiopia (temperature extremes, soil moisture, topography, wind speed and PDSI). Thus comparison of the thematic data present new information about the role of topographic location in climate, meteorological and environmental processes driving drought disasters.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Soil moisture map in Ethiopia in 2018. Mapping: GMT. Source: author</figcaption></figure>
Research tools

Mapping for all illustrations (Fig. 1–7) has been done in Generic Mapping Tools (GMT) cartographic scripting toolset. The GMT based scripting approach applies the principle of programming in terms of console-based generating maps as an efficient cartographic workflow that supports both numerical modelling of the spatial datasets from the tabular formats and aesthetic graphical drawing of maps created for the environmental monitoring of Ethiopia. The regional extent of the study area was applied for Ethiopia, i.e., 33°E to 48°E and 3°N and 15°N.

The data were modelled and interpolated
by raster grids using GMT. The datasets were
captured from the WorldClim and GEBCO
repositories with raster data and Digital Chart
of the World (DCW) with vector data for

clipping the country to highlight the study area.
The mapping process of the GMT based
scripting differs from designing a map using
Graphical User Interface (GUI) based
traditional GIS, such as ArcGIS or QGIS,
because the data processing is being performed
using script from a console. The mapping
includes technically comprehensive and
detailed visualization of the distribution of
continuous fields of data and categorical
environmental parameters in various vegetation
regions of Ethiopia.

The input raster images for each of the
maps (Fig. 2–7) was processed by a set of the
GMT modules where each one defines certain
map characteristics as described in existing
technical papers (Lemenkova, 2020b, 2021a,
2021b).

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Precipitation in Ethiopia in 2018. Mapping: GMT. Source: author</figcaption></figure>
The GMT 'pscoast' modules was used for adding general cartographic elements (rivers and country borders) and 'psscale' was used to plot color explanation legend. The 'psbasemap' module was used to plot a title and a cartographic grid with graticule annotations (here the flag '-Bpxg2f0.5a2 -Bpyg2f2a2 -Bsxg1 -Bsyg1' for each map which shows primary and secondary grid annotations and intervals for the ticks).

To facilitate spatial comparison and cartographic visualization, the maps of the presented series (Fig. 1-7) were reconciled to the Mercator projection and the same spatial extent covering the country of Ethiopia (33°E-48°E, 3°N-15°N). The output environmental raster grids (Fig. 2-7) were recomputed by GMT and the output graphics converted from the PostScript (ps) format to the standard graphical output (jpg) using the fine resolution (720 dpi) as follows: 'gmt psconvert ET_PDSI.ps -A0.5c -E720 -Tj -Z', keeping the same criteria and layout sizes (5.5. inches) used in the implementation of the topographic map (Fig. 1.).

## Cartographic coherency

The template draft of the GMT script designed for all the seven maps of the thematic series enabled visual cartographic coherency for the overall compatibility of the maps. This facilitated the comparative analysis of the continuous fields represented on the maps. Using identical coordinate extent, projection and orientation enabled to compare how differently the variability of the climate parameters behaved with respect to the topographic features of Ethiopia. The emphasis of the cartographic visualization was on the two steps in a technical mapping workflow:

i. how each flag within a GMT script controls the appearance of the cartographic elements (scale bar, grid image, rivers, DEM

hillshade of Ethiopia) and how color palettes on all the maps within a map series;

ii. how sensitive the colour palettes are to climate variability parameters (temperatures, soil moisture, precipitation, PDSI values) demonstrated on the maps.

Therefore, all seven maps were examined how the symbolized environmental variables TerraClimat datasets in one map worked together with other maps of the presented series to enable visual correlation, compatibility and comparability of the maps. The template script of GMT with identical coordinates and map extent was applied for the 7 maps to ensure the iterative technical process of mapping.

## 3. Results and discussion

The paper presented modelled, visualized and mapped climate and topographic data across the Ethiopia ranges. The data were processed by the GMT methods to display visual cartographic quality through the selected colour palettes and vector properties (graticule, text annotations, legend) and meet the map purpose of modelling drought distribution, while maintaining the environmental goals of the research. The presented study had both theoretical environmental and practical cartographic goals. The environmental goal was to develop a conceptual GMT-based environmental analysis for visualization methods to depict data on a series of climate maps in general and the PDSI map in particular. The practical cartographic goal was to develop and present the cartographic functionality of the GMT scripting tool as a geoinformation visualization device and a scripting framework used for climate datasets.

The geoinformatics and data science approach has been taken to both goals using technical functionality from cartographic semantics and data processing, data visualization and aesthetic design.
In the study presented here, twofold methods of data visualization were presented for comparison of the topography and climate variables of Ethiopia, mapped using GMT: i) separable display of environmental variables achieved through series of the separate seven maps; ii) PDSI mapping demonstrating the integral display of the drought parameters, achieved through visualization of the complex PDSI index of drought over Ethiopia.

The results present a series of seven new maps based on the climatic data on Ethiopia, visualized and interpreted alongside the topography: 1) topography; 2) soil moisture; 3) T °C minimum; 4) T °C maximum; 5) Wind

speed; 6) Precipitation; 7) Palmer Drought Severity Index (PDSI). The maps visualize meteorological and climate parameters for ecological monitoring, assessment of climate change, distribution and intensity of droughts.

The precipitation values (Fig. 3.) vary from 0 to 211 mm according to the TerraClim dataset checked up by GDAL. Although the maximal values reach higher values, the mean values dominating over the country are 5.167 (mainly light yellow color in Fig. 3.) and a standards deviation (StdDev) is 11.294.

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** T °C minimum (tmin) in Ethiopia in 2018. Mapping: GMT. Source: author</figcaption></figure>
According to the data inspection by GDAL, there are three peaks of the higher precipitation in Ethiopia, which location is roughly corresponding to the topographic heights: 1) the Ethiopian Highlands; 2) Omo and Mago National Parks; 3) Semien Mts National Park where mean values of the

predominant precipitation values exceed 20 mm. Comparison of the presented figures enables to see the variations of climate and meteorological characteristics of the country with main topographic features of Ethiopia.

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** T °C maximum (tmax) in Ethiopia in 2018. Mapping: GMT. Source: author

The Tmin has values ranging from -1.8 °C to 24 °C, while the Tmax has values ranging from 14.4 °C to 40.2 °C with the maximal areas clearly visible in the SE region of the country and the minimal values primarily concentrated along the Great Rift Valley. The selected color palette is 'lapaz' from the available choice of GMT. A similar pattern of the values distribution can be seen in Figure 4 (Tmin colored by the color palette 'imola' of GMT) showing the lowest values in the Great Rift Valley and over the Ethiopian Highlands (dark blue colors in Figure 4) and highest (up to 24 °C) in the NE region of the country.</figcaption></figure>
It has been observed that the wind speed (Fig. 6.) well reflects the surface topography very well with distribution of lower values (0 to 1 m/s, yellow color in Fig. 6.) being directed almost in meridian direction which corresponds to the general extent of the Ethiopian

In general, the wind speed has lower values (0.5 to 2.0, which corresponds to the yellow to light aquamarine colors in Fig. 6.) in the depressions and topographic lowlands while the lower speed is notable for the higher elevations in the topographic landforms (2.0 to 4.5, which corresponds to the green colors and hues in Fig. 6.). The highest values of the wind speed (above 5.0 m/s) are notable along the coasts of Somali (44.0°E–47.0°E).

Along the Ethiopian Highlands higher values are demonstrated by the soil moisture (Fig. 2.) which has the highest value of 180 (checked by 'gdalinfo -stats et_soil.nc')

Highlands (compare to Fig. 1.). According to the data inspection (gdalinfo -stats etWs.nc), the minimum recorded is 0.100m/s, maximum is 6.1 m/s. The wind speed increases strongly in the depression area which forms a 'delta'-shaped form eastwards of the longitude 39.5°E. decreasing towards the desert areas (Ogaden Desert) representing the rocky and sandy nature of the Triassic deposits. The mean value is about 14.990, standard deviation (StdDev) is 20.056, according to the gdalinfo, which is changing abruptly toward the eastern direction along with the topography of the Ethiopian Highlands (compare Fig. 1. to 2.). Another region of higher values in soil moisture is notable around the Awasa city on the right flank off the Great Rift Valley (Fig. 1.) with values in the interval 40–60 (blue colors in Fig. 2.).

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Wind speed in Ethiopia (2018). Data: TerraClimate. Mapping: GMT. Source: author</figcaption></figure>
The lowest values are notable in the lowlands of the Yangudi Rassa National Park and Semera. A comparison of the presented maps – topographic elevation (Fig. 1.), soil moisture (Fig. 2.), precipitation (Fig. 3.), extremal temperature as minimal (Fig. 4.) and maximal (Fig. 5.), wind speed (Fig. 6.) and the PDSI (Fig. 7.)– at the area surface of Ethiopia, shows a great agreement of the datasets proving the dependence of the PDSI on temperature, precipitation, soil-relief parameters and geomorphology of the study area.

The results show the lowest values of the PDSI (-11.7, a moderately moist area) are found in the NW region of Ethiopian Highlands, while the highest values (2.3, an extreme drought: orange to red colors in Fig.

7.), are recorded in the south-east off the Ethiopian Highlands (compare Fig. 7. to 1.). Fig. 4. and Fig. 5. are showing the extremal values of the temperature (Fig. 4. for the minimal and Fig. 5. for the maximal temperature in 2018) have isolines remarkably correlating to the topographic map (Fig. 1.) with clearly visible ‘delta’-shaped depression area NE to the Greate Rift Valley and the Danakil Depression, where the temperature patterns change accordingly following the topographic relief patterns.

The application of GMT enabled to produce multiple cartographic representations of Ethiopia using several climate grids from a single detailed repository of TerraClimate by scripting approach from a GMT console.

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Palmer Drought Severity Index (PDSI) in Ethiopia. Mapping: GMT. Source: author</figcaption></figure>
Using GMT enables to produce a variety of maps with different purposes, such as geological, geomorphological, climate or environmental maps as scripting method rapidly processes multi-format raw data (IMG, GRD, NetCDF) to high-quality finished mapping outputs. The paper contrasts the existing approaches of the traditional GIS by using more sophisticated approach of GMT based scripting and customized mapping goals to visualization of the environmental parameters of Ethiopia: temperature (minima and maximal), soil moisture, wind speed, topography and the PDSI values.

The presented results explicitly demonstrate the influence of the console-based mapping on the cartographic output by presenting high-quality aesthetic map layouts. The paper shown the cartographic GMT based scripting as an influencing technology that amends cartographic workflow and affects analytical visualization of maps for environmental analysis derived from a multi-source datasets of TerraClimate aimed for multi-purpose environmental and climate assessment of Ethiopia. Comprehensive and detailed maps of the environmental parameters, climate elements and visualization of PDSI are a valuable resource for researchers to assess possible droughts in Ethiopia, a country with a highly contrasting climate and topographic setting.

## Discussion

Mapping spatial data has been a subject of longstanding interest in environmental and climate studies, judging by relevant publications. Data visualization through application of various GIS and methods of cartographic data processing can effect changes in visual understanding of the mapped parameters, finding correlations between the phenomena (precipitation, soil moisture, temperature, PDSI) through analysis of their

logical topology, geometry and distribution of the fields analyzed along with categorical variables, thus most approaches of mapping are designed to highlight depicted objects and better visualize their characteristics. In the presented multi-source data processing that resulted in a series of maps, projection was pre-established and agreed as the same for all the maps from the series, based on the principle of the best look reflecting the extent and topographic features of Ethiopia.

The guiding principle driving the presented research is to produce a series of the seven new thematic maps on climate settings of Ethiopia with a special accent on PDSI. While preserving identity and logical consistency of maps (projections, spatial extent, location) the maps demonstrate different meteorological and environmental variables over Ethiopia: temperature, soil moisture, precipitation and wind speed. This paper introduced a scripting approach which largely contrasts with the existing GIS-based mapping. Hence, the literature review of the existing publications on climate mapping of Ethiopia shown that the solutions offered to visualize environmental settings and map climate data (PDSI, temperatures, soils moisture etc) have been largely based on the traditional GIS approaches (for instance, ArcGIS) while ignored other cartographic approaches (scripting GMT tools).

The synchronized adjustment of cartographic symbols and elements has been applied for standardization of the maps (Fig. 1. to 7.) and achieving cartographic consistency. Pairwise variation in visual categories of the continuous fields using variability on color palettes of temperatures (Fig. 4. and 5.), environment (Fig. 1. and 2.), meteorology (Fig. 3. and 7.) and climate (Fig. 4. and 6.) resulted in multiple representations of their elements as a conceptual approach of semantic mapping applying geometric and graphical multiplicities. The presented series of maps
focused on the interaction between various
environmental, climate and topographic
features of Ethiopia aimed to demonstrate and
highlight strong correlation between the
topographic, climate and meteorological
processes, soil moisture and temperature
distribution over the area which is finally
reflected in PDSI. The map of PDSI,
generalizing and summarizing the
environmental properties of the country
through the computed and visualized index,
effectively highlights the regions of Ethiopia
prone to drought.

Conclusions

The presented thematic maps of Ethiopia
are based on the visualization of the high-
resolution datasets used for interpretation of the
spatial distribution of droughts (PDSI) and
related auxiliary parameters (extreme
temperatures (min/max), speed intensity,
precipitation, soil moisture, topography) in
Ethiopia. The links between the demonstrated
environmental and climatic components based
on visual comparison and overlay of the
cartographic imprints demonstrate
interrelations between these factors. The
correlations between the soil moisture,
precipitation, relief and temperatures point at
their environmental associations affecting
meteorological variables and drought that can
be useful in possible prognosis and modelling
of droughts. The cartographic series of the
thematic climatic and topographic maps
produced using identical spatial extent,
projection and view yet using a variety of color
palettes for each environmental variable can be
successfully used as a reliable source for
ecological monitoring in Ethiopia. In this way,
this paper contributes to the environmental
research of Ethiopia with a special focus and
detailed visualization of PDSI map for drought
modelling.

The demonstrated results show the
predominant distribution of the PDSI values
over the Ethiopia Highlands and mountainous
areas. The spatial distribution of the PDSI and
other climatic datasets covering Ethiopia (as on
2018) obtained as a result of the GMT based
mapping reflects the correlation with
topography as well as the effects of
temperature and precipitation on the variations
of the PDSI values. For instance, higher values
of the PDSI correlates well with distribution of
the areas of the prevailing winds with higher
speed. On the contrary, the lower values of the
PDSI (severe drought) are found on the sites
with low-speed winds. For climate and
environmental research, cartographic
visualization is a primary technical tool of data
processing used to identify geospatial clusters
and regular patterns of meteorological
continuous fields and to compare these features
with patterns of both topographic relief and
potential droughts based on PDSI map. Similar
situation can be seen on the comparison of
maps showing precipitation, temperature and
topography with the PDSI values over the
country. Thus, regions of higher precipitation
coincide with the areas of higher PDSI values
(that is, relatively moist regions), while low-
level precipitation regions correlate with the
PDSI regions showing lower values (that is,
extreme drought). The relationship between the
occurrence of higher/lower temperature values
(min/max as for 2018) in Ethiopia and the
PDSI values has also been noticed by the
comparison of the respecting maps.

The generated maps are required by the
drought assessment and environmental monitoring
studies. These maps are proposed for the first time
on a GMT-based approach based on scripting
techniques and contribute to the existing studies
on environment and ecological monitoring of
Ethiopia with special focus on climate change,
problems of drought and crop harvest (Ntale and
Gan, 2003). A series of the climatic variables (soil
moisture, extremal temperatures (T °C min; and T °C max), wind speed, precipitation; PDSI) have been visualized based on the existing datasets of WorldClim using scripting approach of GMT to show the reference of the climate to the topographic setting in Ethiopia. The assumption made is that the relief elevation, orientation and steepness should be affecting these parameters and which has been visualized on the series of maps and discussed as interpretation of the map values. A presented series of the GMT-based climate and environmental maps associated with the PDSI index of Ethiopia is useful as the base illustrations in future similar research on the environmental assessment and monitoring of the country.

Scripting cartographic concept implemented in GMT enabled to find out optimal cartographic solutions for data visualization that support effective map production workloads through minimizing human-made routine and increasing automatization. Incorporating various multi-source datasets into the map series, as presented in this research, extends the range of variables for a regional extent of Ethiopia, which in turn increases the volume of information to maintain analysis of correlation between the climate and topographic parameters for the environmental monitoring of Ethiopia.

## Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Acknowledgement

The author thanks the anonymous reviewers and editor for comments, review and editing of this manuscript.

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