Relationship between climate and land use land cover change over West Africa

doi:10.21203/rs.3.rs-1987298/v1

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Relationship between climate and land use land cover change over West Africa

https://doi.org/10.21203/rs.3.rs-1987298/v1

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The relationship between climate and land use land cover change over West Africa has often been assessed with climate simulations, although the model-based approach suffers from the limitations of climate models specifically for West Africa. In this paper, an alternative approach based on physical analysis of historical land cover data and standardized climatic indices is used to investigate climate-land interactions to establish the climatic thresholds and their corresponding land use impacts. Annualized land change intensities and the climatic indices are first estimated separately and then linked at various spatiotemporal scales. The result shows that climate-induced land cover change results from abrupt changes in climatic conditions. A regional change of (-1.0–1.0)\(℃\), (0–1.5)\(℃\),(-0.5–0.5)\(℃\), and up to \(\pm\)50 mm changes in precipitation and climatic water balance leads to (45039–52133) km², (20935–22127) km² and approximately 32000 km² changes respectively, while a \(\pm\)0.5\(℃\) and \(\pm\)20 mm change represents normal climate conditions with changes below 20000 km². Conversely, the plausible pathways through which West African land surface impacts the climate is the conversion of cropland, forest, grassland, and shrubland. The average climatic risk ranges from − 0.025 to 0.025 yr⁻¹ while the probability of occurrence ranged variably from 0 to 0.833. The results offer the basis to re-evaluate land and climatic information necessary for improving the reliability of climate models over West Africa. For sustainable development, this work reveals the need for policy-driven interventions for efficient resource management and the prevention of degradation and deforestation in the region.

Climate Analysis and Modeling

ESA CCI LC maps

Trajectory analysis

Standardized Temperature Index (STI)

Standardized Precipitation Index (SPI)

Standardized Precipitation Evapotranspiration Index (SPEI)

Sustainable Development

Climate change and land cover change are the two most important drivers leading to global environmental change. On the one hand, land cover change is strongly influenced by global climate changes due to land-atmosphere coupling. On the other hand, climate change influences land use changes which result in changes in agro-ecological zones or bioclimatic zones in the long term. Determining the interactions between land use and climate and the associated impacts is however challenging owning to the complex and mutual interaction between climate and land (Dale, 1997; Gao and Liu, 2011). The challenges range from approaches to quantifying the climate-land changes and their associated impacts, reconciling the regional differences in climate-land interactions, and determining which driver will play a dominant role in the near and far future.

Investigating the climate change-land use change relationship, Dale (1997) concluded that land use changes have greater ecological effects than climate change. Recent studies using predictive models however show that climate change will prevail over land use change in some species distribution and abundance in the future (Barras & Braunisch, 2021; Préau et al., 2021). Meanwhile, Selwood et al. (2014) noted that while it is globally recognized that landscape conditions account for species decline and loss, climatic factors had equally strong effects on demographic rates in animal and plant populations so the combined pressures of land-use change and climate change may result in species declines and extinctions occurring faster than otherwise predicted, particularly if their effects are multiplicative. In other research, Findell et al. (2017) found an increase in hot and dry summers from once in a decade to every 2 to 3 years in response to the conversion of mid-latitude forests to pastures and croplands. Investigating the impact of the resolution of vegetation model on regional climate, Sylla et al. (2016), show that a high-resolution vegetation cover significantly improves the simulation of important features of the West African Monsoon. In some other research, concerns have been raised about the validity of using the difference in NCEP/NCAR reanalysis and observed surface temperature as a measure of the impact of urbanization and land use changes on climate in parts of the united states (Kalnay et al., 2003). From the foregoing, the climate-land use related research may be grouped into three categories which include (i) investigating the impact of climate change on land use change (Gao and Liu, 2011; Pipitpukdee et al., 2020; Pramudya et al., 2016; Wajid et al., 2020); (ii) investigating the land use impact on climate change (Findell et al., 2017; Hou et al., 2021; Sylla et al., 2016), and (iii) investigating the impact of the combined effects climate change and land use change on other environmental variables using modeling tools (Alamdari et al., 2022; Amir et al., 2021; Gabiri et al., 2019; Hung et al., 2020; Selwood et al., 2014; Tirupathi and Shashidhar, 2020).

Modeling tools have become useful to simulate the combined effects of climate-land interactions but also help to establish mechanisms leading to various environmental outcomes including biodiversity (Schulte to Bühne et al., 2021; Srinivasan and Wilcove, 2021), population (Molotoks et al., 2021), pathogens (Casazza et al., 2020), and food security impact of elevated CO₂ (J. Liu et al., 2021), just to mention a few. Modeling tools have dominated climate-land related research due to their ability to model complex interactions and better represent environmental concerns such as streamflow and hydrological predictions, pollutant loading, water balance, and water quality (Achugbu et al., 2021; Alamdari et al., 2022; Boone et al., 2016; Cao and Dan, 2021; Chilukoti & Xue, 2021; Gabiri et al., 2020; Hung et al., 2020; Idrissou et al., 2022; Jain et al., 2021; Nyatuame et al., 2020; Ross and Randhir, 2022; Selwood et al., 2014; Srinivasan and Wilcove, 2021; Sylla et al., 2016; Tirupathi and Shashidhar, 2020; Tu, 2009), amongst others. Despite the indispensability of modeling tools, they are not without problems. Firstly, the land component of climate-land simulations often uses one year of historical data and remains static neglecting the interactions between land use changes and climate variations throughout the simulation period (Deng et al., 2014). While this may be attributed to the need for extra computational resources for such simulations, it is also due to the lack of global consistent land cover data with high spatial and temporal resolution. Secondly, as the focus of model-based climate-land simulation is often the impact of a third environmental variable, the direct impact of land use on climate and vice versa are often rarely simulated or unreported in most cases. Alternative to the model-based climate-land simulations is the use of analysis to assess the direct impact of climate on land changes and vice versa (Gao and Liu, 2011; Hou et al., 2021; Wajid et al., 2020). While the analysis-based approach may be significantly limited than the model-based approaches in general and in terms of future prediction capabilities, it allows for analysis of climate variations with historical land use land cover changes. It can be the basis for validating model-based simulations over the historical period and the development of climate-land use change scenarios based on historical land-climate data.

In this work, we investigated the link between historical land cover changes and extreme climatic indices over West Africa using European Space Agency (ESA) Climate Change Initiative (CCI) annual 300m land cover maps (hereafter ESA CCI LC maps or ESA CCI maps of CCI maps) from 1992 to 2019 and multiple climate datasets between 1980 and 2020. Like any region, the land use land cover change modulates the West African climate due to the strong land-atmosphere coupling. Conversely, climatic changes influence strongly the land use changes due to the largely rain-fed subsistence agriculture in this region and the low adaptive capacity. Extreme climatic conditions have even more serious implications on agricultural and food production. Despite these foreseen problems, very few studies have attempted to assess the regional climate response to land cover characterization over West Africa (Achugbu et al., 2021; Boone et al., 2016; Sylla et al., 2016), and probably no research based on physical analysis of land cover change and climate variation. This paper aims to investigate the impact of climatic on land use land cover change and vice versa and characterize the temperature, precipitation, and climatic water balance thresholds leading to land surface area changes. The paper proceeds in three stages which (1) quantifies land use land cover changes and land use sustainability development indicators using trajectory and change analysis, (2) characterizes climatic conditions using the STI, SPI, and SPEI, and (3) establishes a link between climatic changes land land use changes at various spatial and temporal scales. The outcome establishes threshold-based climatic conditions leading to abrupt land cover changes over the West African region. This threshold provides the basis for comparison with climate-land-model-based simulation results over the region and is important for parameterizing climate changes and land area changes for model simulation over the region. More importantly, this study identifies specific areas where policy reformation and policy-driven interventions are needed to protect the West African space and ultimately promote sustainable development through efficient management of land resources in the region.

2.1. Study area

West Africa may be subdivided into four main climatic zones (Fig. 1) namely the Guinean zone (18\(^\circ \text{W}-18^\circ \text{E},4^\circ \text{N}-8^\circ \text{N}\)), Sudano-Sahel zone (18\(^\circ \text{W}-18^\circ \text{E},8^\circ \text{N}-12^\circ \text{N}\)), Sahelian zone (18\(^\circ \text{W}-18^\circ \text{E},12^\circ \text{N}-16^\circ \text{N}\)), and Saharan zone (18\(^\circ \text{W}-18^\circ \text{E},16^\circ \text{N}-20^\circ \text{N}\)) (Annor et al., 2022). The Guinea region is a seasonally wet-and-dry deciduous or semi-deciduous forest, characterized by annual rainfall between 1200–2200 mm with a 7 to 8 months distinct dry season. The Sudanian region is mostly open woodland, open tree savannas, and wooded savannas with annual rainfall between 600 and 1200 mm and a 5 to 7-months dry season. The Sahelian region is characterized by a mixture of steppe and short grass savanna (open herbaceous types) and woody plants, and an average rainfall of 150 and 600 mm with 8 to 9 months of dry season. The annual average rainfall of the Saharan region ranges from 0 to 150 mm with vegetation varying from sparse to absent except in wadis, oases, and depressions, where water is present at or just below the surface. The West African climate is related to the advanced north-to-south retreat of the intertropical front which is associated with rainfall migration between the south and north latitude. Temperatures vary a little in the South, whereas temperature ranges from 0 to over 45˚C in the arid north (Cotillon and Tappan, 2016). The West African land surface is complexly influenced by topography, climate, vegetation, geology, hydrography, and human activities which together determine land cover and land use changes in the region. Rapid population growth has led to the transformation of forests, woodlands, steppes, and savanna into agricultural lands to feed the growing population.

2.2 Data

2.2.1 Land cover data

The ESA CCI LC dataset is a timeseries of globally generated annual land cover maps at 300m spatial resolution covering initially 1992 to 2015 (Jiang and Yu, 2019; Li et al., 2018; S. Liu et al., 2021; X. Liu, Yu, Li, et al., 2018), with a continuous update to near present. The ESA CCI dataset was produced from a classification of MERIS (Medium Resolution Imaging Spectrometer) Full and Reduced resolution data archive from 2003 to 2012 into a unique 10-year baseline LC and updating and backdating the baseline maps to produce 24 annual LC maps. The overall weighted-area accuracy is about 71.1% (X. Liu, Yu, Sia, et al., 2018), with an overall thematic accuracy of 74.4% based on 2010 maps (Guidigan et al., 2019). For West Africa, the highest correspondence is found in the Saharan region, and the lowest correspondence in the dry Savanna and Sahel regions (Tsendbazar et al., 2015). The CCI maps are based on the United Nations Food and Agriculture Organization (UN FAO) land cover classification scheme (LCCS) and can be obtained in TIFF format with 37 classes at http://maps.elie.ucl.ac.be/CCI/viewer/ or as 22 classed NetCDF format at https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview), which is also recommended for use by the climate modeling community (Reinhart et al., 2021).

Table 1

Product information of ESA CCI LC
Temporal coverage	Sensors	Initial resolution	Final resolution	Temporal resolution
2003 − 2012 Baseline production	MERIS FR/RR	300 m	300 m
1992 − 1999	AVHRR	1km	300 m	1 year
1999 − 2013	SPOT-VGT	1km	300 m	1 year
2013 − 2015	PROBA-V	1km	300 m	1 year
AVHRR (Advanced Very High-Resolution Radiometer), SPOT-VGT (Systeme Probatoire d’Observation de la Terre Vegetation), PROBA-V (Project for On-Board Autonomy V), MERIS (Medium Resolution Imaging Spectrometer)

2.2.2 Climate data

Temperature (T), precipitation (P), and potential evapotranspiration (PET) datasets between 1980 and 2020 were used for this study. Table 2 summarizes the data products, spatial resolution, period analyzed, links to the datasets, and publications containing technical validation reports. For consistency and to ease comparison, only data products that have all three variables were used. In the case of CHIRPS data which has no PET, the Global Land Evaporation Amsterdam Model (GLEAM) (Peng et al., 2020) and CHIRPS precipitation dataset to derive a high-resolution SPEI dataset for the West Africa region similar to the high-resolution SPEI for Africa which available at (https://data.ceda.ac.uk/neodc/spei_africa/data).

Table 2

Temperature, precipitation, potential evaporation, and potential evapotranspiration datasets used
Datasets	Variable	Spatial resolution	Period used	Link to dataset/ Technical validation report
CRU TS	T	0.5\(^\circ \times 0.5^\circ\)	1980–2020	https://crudata.uea.ac.uk/cru/data/hrg/cru_ts 4.05/cruts.2103051243.v4.05/ (Harris et al., 2020)
ERA5-L	T	0.1\(^\circ \times 0.1^\circ\)	1980–2020	https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly means?tab = overview/ (Muñoz-sabater et al., 2021)
TCM	T	1/24°~4km	1980–2020	http://thredds.northwestknowledge.net:8080/thredds/terraclimate_aggregated.html/(Abatzoglou et al., 2018)
CHIRTS	T	0.05\(^\circ \tilde5\text{k}\text{m}\)	1983–2016	http://data.chc.ucsb.edu/products/CHIRTSdaily/v1.0/africa_netcdf_p05/(Verdin et al., 2020)
CHIRPS	P	0.05\(^\circ \tilde5\text{k}\text{m}\)	1981–2020	https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p05// (Funk et al., 2015)
CRU TS	P	0.5\(^\circ \times 0.5^\circ\)	1980–2020	https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/cruts.2103051243.v4.05/(Harris et al., 2020)
TCM	P	1/24°~4km	1980–2020	http://thredds.northwestknowledge.net:8080/thredds/terraclimate_aggregated.html /(Verdin et al., 2020)
CRU TS	PET	0.5\(^\circ \times 0.5^\circ\)	1980–2020	https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/cruts.2103051243.v4.05/ (Harris et al., 2020)
ERA5-L	PET	0.\(1^\circ \tilde 11.1\text{k}\text{m}\)	1981–2020	https://data.bristol.ac.uk/data/en_GB/dataset/ qb8ujazzda0s2aykkv0oq0ctp/resource/8c5bfc47-c0df-4778-ba54-31f5bd42b648/ (Singer et al., 2021)
TCM	PET	1/24°~4-km	1980–2020	http://thredds.northwestknowledge.net:8080/thredds/terraclimate_aggregated.html/(Verdin et al., 2020)
GLEAM	PE	0.25\(^\circ \tilde27\text{k}\text{m}\)	1981–2020	sftp://hydras.ugent.be with username, password, and port/ (Martens et al., 2017)
CRU TS – Climatic Research Unit gridded Time Series; ERA5-L – ERA5-Land; ERA5 – ECMWF ReAnalysis fifth-generation; ECMWF – European Centre for Medium-Range Weather Forecasts; CHIRPS – Climate Hazards Group Infrared Precipitation with Stations; TCM – TerraClimate; GLEAM – Global Land Evaporation Amsterdam Model

2.3 Methods

The method for investigating the climate-land relationship in this work is classified into initial, intermediary, and final stages (Fig. 2). The initial stage comprises trajectory analysis to identify changed pixels from 1992 to 2019 and aggregation of these changed pixels by land cover type, climatic zone, and temporal evolution to produce land cover transitions, zonal evolution, and annualized land cover change intensities, respectively. In the intermediary stage, the trends.earth tool in Quantum GIS (QGIS) software is used to compute sustainability indicators (trajectory, change, and performance) and a further extraction and aggregation of these indicators by climatic zones (using changed pixels coordinates identified at the initial stage). In the final stage, years with spikes in land cover change area are identified using annualized land cover change intensities which were determined at the initial stage, and the climatic conditions leading to those spikes are also investigated at various temporal and spatial scales using the standardized climatic indices (STI, SPI, and SPEI). These climatic conditions are used to establish the climatic thresholds leading to abrupt land cover changes in West Africa. The climatic conditions leading to abrupt land cover changes were validated by comparing with normal climatic conditions with no abrupt changes in land cover area. Finally, the occurrence frequency and their probabilities are used to determine the frequency and probability of occurrence of the various STI, SPI, and SPEI indices, while the slope of the Mann Kendall trends test is used to compute the average climatic change rate over West Africa.

2.3.1 Land cover change analysis

The ESA CCI LC maps are available from 1992 to the near present, however, the maps were available from 1992 to 2019 at the time when this analysis was initiated. Version 4.3 (http://maps.elie.ucl.ac.be/CCI/viewer/download.php) of the ESA CCI user tool dedicated for sub-setting, re-projecting, resampling, and converting the LCCS legend to user-specific Plant Functional Tables (PFTs) was used to subset the annual CCI maps (1992–2019) to the West Africa domain. The Climate Data Operator (CDO) function “merge” was used to concatenate the annual maps into a single file containing the individual maps layers. With similar functions in CDO, a trajectory analysis (X. Liu, Yu, Sia, et al., 2018; Zhou et al., 2008) of each pixel in the ESA CCI maps from 1992 to 2019 was done to assess the total changed pixel within West Africa. Each changed pixel with longitude, latitude, and the total number of class information was exported to a text file for further analysis with R software. The number of changes per pixel was estimated as one less than the total number of land classes within that pixel. Thus, if a pixel has 3 different land cover classes from 1992 to 2019 then the total change possible within the period is 2. The area corresponding to the total changed pixels was calculated by multiplying the total changed pixels by 0.09: the area equivalent of the 300m pixel in square kilometers. To quantify land cover area change variation along the latitudes we computed to the zonal sum (from 18°W to 18°E) of all changed pixels. The cumulative area change for each climatic zone was computed by aggregating the area change for each respective zone: 4°N − 8°N for the Guinean zone, 8°N-12°N for the Sudan zone, 12°N-16°N for the Sahel zone, and 16°N-20°N for the Sahara zone. To account for latitudinal variation for the 1-time, 2-times, and 3-times changes, each was aggregated separately along the latitude. For the assessment of the drivers of land cover change, we classify the transformation of each pixel from 1992 to 2019 as either “human-induced”, “natural change” or unchanged considering the land cover types at the start and the end years. The total changed pixels along the latitude were disaggregated into natural, anthropogenic, or unchanged changes by adopting the classification scheme of Zhou et al., 2008, considering the plausible drivers of land use and land cover changes in the West African context (Fig. 3b). For such categorization, the land cover types used in this work are largely grouped into forests (F), grassland (G), cropland (C), wetland (W), urban (U), bare (B), and water (W). The unchanged classes (F\(\to\)F, G\(\to\)G, C\(\to\)C, Wl\(\to\)Wl, U\(\to\)U, B\(\to\)B, W\(\to\)W) are represented by the diagonals of the matrix in Fig. 3b. Human-induced changes are decisive changes that include the conversion of any of the land cover types to cropland (F\(\to\)C, G\(\to\)C, Wl\(\to\)C, U\(\to\)C, B\(\to\)C, W\(\to\)C) and to urban (F\(\to\)U, G\(\to\)U, Wl\(\to\)U, B\(\to\)U, W\(\to\)U) respectively, which represent the human impact on the environment. Other land conversions considered mostly anthropogenic include the conversion of urban and bare areas to other land cover types such as (U\(\to\)F, U\(\to\)G, U\(\to\)C, U\(\to\)Wl, U, U\(\to\)B) and (B\(\to\)F, B\(\to\)G, B\(\to\)C, B\(\to\)Wl, B\(\to\)U). Conversions to grasslands are unintentional in the West African space and are therefore mostly considered an influence of natural factors in this work. Similarly, conversions to water and wetlands are either natural or naturally induced in the West African space and have therefore been classified as such except U\(\to\)Wl which often results from displacement of a population due to the construction of hydroelectric dam reservoirs. Forest conversion are often intended and as such considered mostly a human-induced change. Finally, unlikely transitions such as W\(\to\)F, Wl\(\to\)F, F\(\to\)Wl, G\(\to\)Wl, and C\(\to\)Wl are considered natural changes in West Africa. For the classification of land use and land cover changes according to state, we adapted the land use/land cover conversion matrix for six IPCC classes with 30 possible transitions (Sims et al., 2019), available in the trends.earth (International Conservation, 2018) plugin within QGIS to show the state (improvement, stable, or degradation) and land cover loss type of each changed pixel. Both spatial and temporal information on the state of land cover as well as the land cover loss type over West Africa is based on Fig. 3a. The latitudinal variation of land cover state and the loss types are also obtained from this information.

2.4 Climate indices

The Standardized Temperature Index (STI), Standardized Precipitation Index (SPI), and Standardized Precipitation and Evapotranspiration Index (SPEI) are the main climatic indices used to assess the relationship between land cover change and climatic changes. A 12-monthly timescale was chosen for the computation of the climatic indices as the land cover data used has an annual temporal resolution.

2.4.1 Standardized Precipitation Index (SPI)

The SPI developed by McKee et al. (1993), and updated by Edwards and McKee, (1997) is the most widely used drought indicator to measure meteorological drought at different timescales. The SPI values range from − 3 to + 3 (Mortey et al., 2019) and represent the number of standard deviations by which observed precipitation deviated from the climatological mean. For a detailed mathematical formulation and computation of SPI refer to Edwards and McKee (1997), and Liu et al. (2021). For this work, we employ the SPEI package in R software to compute the SPI.

2.4.2 Standardized Precipitation Evapotranspiration Index (SPEI)

The computation of SPEI uses a similar procedure as SPI except that instead of precipitation, the SPEI computation uses the Climatic Water Balance (CWB) which is the difference between precipitation and potential evapotranspiration. The CWB allows accounting for the temperature factor which is often neglected in the computation of SPI. For the mathematical formulation and computation of SPEI refer to Vicente-Serrano et al. (2010). The procedure for SPEI computation is also presented by Liu et al., 2021 and https://spei.csic.es/home.html. In this work, the SPEI package in R Software is employed for all SPEI computations. The cumulative probabilities for various SPI, SPEI, and their interpretations can be summed as follows.

Table 3

SPI and SPEI-based classification for the various drought categories
SPI/SPEI	Cumulative Probability	Interpretation	Classification	Probability of occurrence
+ 2.0	0.9772	Extremely wet	SPI/SPEI \(\ge\) +2.0	0.023
+ 1.5	0.9332	Very wet	+ 1.5 \(\ge\) SPI/SPEI < + 2.0	0.044
+ 1.0	0.8413	Moderately wet	+ 1.0 \(\ge\) SPI/SPEI < + 1.5	0.092
0.0	0.5000	Near normal	-1.0 > SPI/SPEI < + 1.0	0.682
-1.0	0.1587	Moderately dry	-1.0 \(\le\) SPI/SPEI > -1.5	0.092
-1.5	0.0668	Very dry	-1.5 \(\le\) SPI/SPEI > -2.0	0.044
-2.0	0.0228	Extremely dry	-2.0 \(\le\) SPI/SPEI	0.023

2.4.3 Standardized Temperature Index (STI)

The STI is the temperature equivalent of the SPI and closely related to the principles originally described McKee et al. (1993). In this work the STI package in R is employed for STI computations. The STI various values, their cumulative probabilities, interpretation, classification and probability of occurrence are summarized in Fig. 4 below.

Table 4

Classification of drought indices based on Standardized Temperature Index values
STI	Cumulative Probability	Interpretation	Classification	Probability of occurrence
+ 2.0	0.9772	Extremely hot	STI \(\ge\) +2.0	0.023
+ 1.5	0.9332	Very hot	+ 1.5 \(\ge\) STI < + 2.0	0.044
+ 1.0	0.8413	Moderately hot	+ 1.0 \(\ge\) STI < + 1.5	0.092
0.0	0.5000	Near normal	-1.0 > STI < + 1.0	0.682
-1.0	0.1587	Moderately cold	-1.0 \(\le\) STI > -1.5	0.092
-1.5	0.0668	Very cold	-1.5 \(\le\) STI > -2.0	0.044
-2.0	0.0228	Extremely cold	-2.0 \(\le\) STI	0.023

2.5 Linking land-climate changes

To link land changes to climatic changes, the annualized land cover change intensities were used to differentiate years with abrupt land cover changes from years with non-abrupt land cover changes. The climatic conditions for the former become the climatic thresholds leading to abrupt land cover changes while the climatic conditions for the latter became the basis for identifying climatic conditions leading to non-abrupt changes in land cover area (normal climate). The normal climatic condition was also used as the basis to access (validate) if indeed abrupt changes in the land cover area resulted from abrupt changes in climatic conditions.

2.6 Average climate rate of change

To compute the climatic change rate, the slope of the Mann-Kendall trend test for each of the climatic indices was computed. The Mann-Kendall test is a widely used non-parametric test used to identify if monotonic a trend exists within a time series and has been employed in many SPI-related studies (Danandeh Mehr and Vaheddoost, 2020; C. Liu et al., 2021). The mathematical background is also presented in detail by several studies (Akpoti et al., 2016; Yildirim and Rahman, 2022). For this work, we performed the Mann-Kendall trend test using the “spatialEco” package in R and extracted the slope attribute to show on average how much the climate of West Africa is changing each year based on the STI, SPI and SPEI.

2.7 Climatic indices occurrence frequency and probability of occurrence

The occurrence frequency of the STI, SPI, and SPEI at 12-monthly time scales was estimated using the range of values that defines their classification (Table 3 and Table 4). Thus, the occurrence frequency of an extremely hot (extremely dry) climate is a count of all STI (SPI/SPEI) values greater than or equal to 2 within the period of investigation. The probability of occurrence of the extremely hot (extremely dry) climate is the count of all such occurrences divided by the total period (in months). Thus, if extremely hot (extremely dry) conditions occur 67 times from 1980 to 2020 (492 months) at a location, then the occurrence frequency is 67 at that location, and the probability of occurrence is 67/492 ~ 0.14. The probability of occurrence defines the risk of exposure of a specific location to the various climatic condition defined by the STI, SPI, and SPEI conditions.

A trajectory analysis of each pixel of the ESA CCI land cover maps from 1992 to 2019 shows 5,433,127 changed pixels out of approximately 74.5 million pixels per year \(\times\) 28 years of total pixels analyzed over the West Africa domain. The changed pixels represent approximately 7.28% (488,981 km²) of the entire domain analyzed and consist of 1-time changes (97.989%), 2-times changes (2.097%), and 3-times changes (0.005%). The remaining 92.92% (~ 5,048,462 km²) is stable (unchanged) with the degraded parts being mostly within the Guinean, Sahel, and Sahara zones while the improved portions are found in the Sudan zone (Fig. 4b). In terms of the drivers of land cover change, anthropogenic activities dominate the West African region with natural causes being mostly within the Sahel and Sahara zones (Fig. 4c). Cropland and grassland losses are predominant over the region with tree cover loss mostly within the Guinean zone (Fig. 4d). In the subsequent sections, we present the spatial distribution of the land cover changes and drivers in Fig. 4 along the latitude from the Guinean zone into the Sahara zone.

3.1 Latitudinal change occurrence frequency

The total land cover area change along the latitudes is shown in Fig. 5a and the cumulative sum per climate zone is also shown in Fig. 5b. The Guinean zone registers the least land cover change area of approximately 77,794 km² whilst the Sudan zone records the highest land cover area change of about 167,071 km² (Fig. 5b). Land cover change area increased nearly monotonically along the latitude in the Guinea zone into the Sudan zone until about 9\(^\circ\) N (Fig. 5a). Within the Sudan zone, the land cover decreased generally with latitude but this decrease is not monotonic but exhibits fluctuations towards the Sahel zone (Fig. 5a). The Sahel zone experience a greater land cover area change after the Sudan zone with a total land cover change area reaching 148,217.2 km² (Fig. 5b). The total area changes peak at about 17\(^\circ\) N and decline thereafter to 20\(^\circ\) N. The overlapping of the total changes (in blue) and the one-time changes (in yellow) shows that the majority of the land cover changes occurred just once (Fig. 5a). The two-time and three-time changes lie almost along the zero line from the Guinean to Sahara also implying that the two-time and three-time changes are very minimal compared to the one-time changes. The comparable heights of the blue and yellow bar charts in Fig. 5b also emphasize one-time changes are dominant for each climatic zone. The two-time changes are prominent from the mid-Sahel to near 18\(^\circ\) N and explain the temporal separation of the blue and yellow lines in Fig. 5a. The increase of the two-time changes from 16\(^\circ\)N to 18\(^\circ\) N is responsible for the increased land cover area along the same latitudes. To sufficiently link land cover change to natural and anthropogenic changes, it is important to disaggregate the changes as natural, anthropogenic which is the focus of the next sections.

3.2 Land cover change drivers

From 4\(^\circ\)N to about 13.5\(^\circ\)N, the majority of total land cover change area is human-induced indicating human activities are responsible for the observed changes. Around 13.2\(^\circ\)N the unchanged, human-induced, and natural changes intersect indicating equal contributions of each driver, then the unhanged and human-induced changes continuously interact (intersecting) until they establish maxima (highest peak) at about 17\(^\circ\)N and separate thereafter. Within the latitudes (13\(^\circ\)N-17\(^\circ\)N) there is an amplification of the natural change signals and therefore these latitudes must be interesting for investigating the impact of natural changes which includes the role of climate change on land use and land cover change. The cumulative distribution of land cover change area in Fig. 6b above shows that human-induced changes (in red) are dominant for each climatic zone. From the foregoing land cover changes in West Africa are primarily human-induced with natural induced change contributions from alternating peaks along the latitudes strongest from 17\(^\circ\)N.

3.3 Changed state

From Fig. 7a the majority of changes that occurred are stable (in blue) with minimal degradation (in brown) or improvement (in green). A few latitudes from the start of the Guinean zone, the land cover changes tend to be an improvement until about 6\(^\circ\)N where stable changes tend to increase drastically, dominating the improved changes. The entire Sudan zone is dominated everywhere by stable changes (in blue) with minimum improved and degraded changes (Fig. 7a,b). The stable changes peak around 9\(^\circ\)N and then decrease thereafter into the Sahelian zone until about 13.5\(^\circ\)N where improved and stable changes intersect. From about 13.5\(^\circ\)N to 16\(^\circ\)N improved and degraded conditions are nearly equal while stable changes continue to increase into the Sahara zone. The Sahelian zone appears to be the most dominated by both stable, improved and degraded changes where all three changes closely interact near 14.5\(^\circ\)N. The Sahara zone features the peak changes: a higher peak at about 16.5\(^\circ\)N where most changes appear to have occurred and towards 18\(^\circ\)N in the Sahara where the minimum changes are observed. Within the entire West African space two major peaks are featured; one in the Sudan zone and the other in the Sahara zone with intermittent peaks along the latitudes. While the peak in the Sudan zone is mainly from stable land cover changes, that within the Sahara zone has contributions from improved and stable conditions and as such is the highest change observed in the entire West African space. This observation is also true for all intermittent peaks except for the Sudan zone which is dominated completely by stable changes.

3.4 Land cover loss type

The major land cover losses in West Africa are losses in tree cover and cropland as can be seen in Figs. 8a,b. Grassland and wetland losses are predominant in the Guinean and Sudan zone but decreased drastically in the Sahel and Saharan zones. Generally, tree-cover loss increased from the Guinean zone to about 9.5\(^\circ\)N in the Sudanian zone, then declined thereafter until about 13.5\(^\circ\)N in the Sahelian zone. The Sahelian zone sees nearly as much tree-cover loss as cropland loss while the Saharan zone experienced mostly tree-cover loss. The presence of significant other losses shows the uncertainties in the land cover loss types increase latitudinally from South to North over West Africa in the ESA CCI LC maps. These losses are losses due to shrubland, sparse vegetation, and bare and water losses according to the IPCC classification system.

3.5 Land cover transition

Figure 9 summarizes the land cover transition from 1992 to 2019 over West Africa using the IPCC land cover classification scheme. The transitions based on the other two classification schemes; Global Land Cover-SHARE (GLC-SHARE) and ESA-CCI LC harmonized with the IPCC classification scheme is shown in the appendix with interactive versions at the online version of the manuscript. For simplicity, the transitions were aggregated at 7-years intervals such that only the total transitions from 1992 to 1999, 1999 to 2006, 2006 to 2013, and 2013 to 2019 are shown in Fig. 9. Irrespective of the classification scheme used, wetland, urban, bare, and water have equivalent area changes for all transition periods except from 2016 to 2019 (see interactive Sankey diagrams) which is only six years instead of seven. Since changes in wetland, urban, bare, and water remains constant, irrespective of the classification scheme used, the ESA CCI LC map does not capture changes related to these classes and so hinders their use for detecting changes related to these four land cover classes. Only cropland, forest, and spare vegetation show variation in area changes and so form naturally the realistic classes to investigate the climate response to land use changes over West Africa. Our analysis of climate impact on land cover changes will thus be restricted to cropland, forest, and sparse land (grassland, shrubland, and sparse vegetation).

3.6 Annualized changes in land cover over West Africa

The land cover change area varies from one year to the other with spikes in 1995, 2000, 2004, and 2016, 2018. For each of these years, the spikes observed are consistent for each of the climatic zones showing that it is not a localized event restricted to any of the climatic zones. Instead, it could be more related to a more regional event such as annual climatic conditions. In the next sections, the standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and the standardized temperature index (STI) are employed to investigate climatic conditions leading to these spikes in land cover change area.

3.7 Climatic conditions leading to significant land cover change area

The STI, SPI, and SPEI values for CRU, CHIRPS, CHIRTS, and TERRAClimate datasets at a 12-month timescale are shown in Fig. 11 below. While the climate analysis covers 1980 to 2020, the land cover change analysis covers only 1992 to 2019. Consequently, the period for investigating the climatic relationship with land cover changes was also restricted to the period for which the land cover data was available (1992 to 2019).

The vertical lines (in gray) are the years for which abrupt land cover changes (Fig. 11) have been experienced. The STI values of the CHIRTS, CRU, ERA5-Land, and TerraClimate datasets show the climate of West Africa from 1980-to 2020 may be divided into two episodes; a relatively cold episode which is between 1980 and 2000, and a relatively warm episode from 2000 to 2020 with minor breaks in between these episodes for all three datasets. Within the cold episode, the highest land cover change area occurs: (a) when there is an abrupt change from extremely cold or very cold climatic conditions to moderately cold or near-normal climatic conditions with an increase in STI values as in 1995 (Fig. 11a-p); (b) when there is a change from a hot climate with a positive STI value to a cold climate with negative STI values as in 2000. During the hot episode, the highest land cover change area occurred (c) when there is a sudden change from a nearly normal climate to a hot climate with an increase in STI values as shown in 2004 and 2018 (Fig. 11a-p) (d) when the STI increase from near-normal climate attaining a maximum as in 2016 (Fig. 11a-p). In terms of the SPI, the conditions for abrupt land cover changes occur: (a) when there is an abrupt change from extremely dry to near normal as in 1995 (Fig. 11q-af); (b) when the SPI value attains a peak (maxima) in changing from a wetter climate (with positive SPI value) to a drier climate (with negative SPI value); (d) when the SPI value attains a peak (maxima) value in changing from a drier climate (with negative SPI value) to a wetter climate (with positive SPI value). From the analysis of STI and SPI, the temperature and precipitation conditions leading to abrupt land cover changes can be summarized as (a) a condition of increasing temperature and increasing precipitation, (b) decreasing temperatures and increasing precipitation (c) critical precipitation and temperature decreases or increases. Figures 11(ag) to 11(av) show the SPEI response to the temperature and precipitation changes discussed above. The decrease and increase in temperature from 1994 to 1995 and 2015 to 2016 respectively with a general increase in rainfall resulted in increased SPEI with a corresponding increase in the land cover area change for 1994 and 2016. The decrease in temperature with an increase in precipitation from 1999 to 2000 and 2003 to 2004 led to an increase in SPEI and total land cover change area in 2000 and 2004. It is worth mentioning that the contrasting temperature changes for 1994–1995 and 2015–2016 have similar effects of increased land cover area change between 20935–22127 km² over West Africa. The time-latitude and time-longitude Hovmoller plots below show that most abrupt land cover changes (in red vertical lines) occur mostly with increasing SPI and SPEI, sandwiched on both sides by low SPI and SPEI values along the latitudes and longitudes respectively. The STI re-emphasized the migration from a relatively cold climate to a warm climate from 1980 to 2020 with the most abrupt land cover changes (in red) occurring with increasing or decreasing STI values. Thus, irrespective of the dataset used, the STI, SPI, and SPEI indicate the most abrupt land cover changes experienced are in response to abrupt changes in climatic conditions.

3.8 Relationship between climate and land use and cover change: quantifying the changes

The general conditions leading to land cover changes in West Africa have been presented. The changes in temperature, precipitation, and climatic water balance leading to these observed changes are quantified in this section. From Fig. 10 and Fig. 13a change of up to (0 to 1.5)\(^\circ\)C (1995 − 1994,2016 − 2015) leads to a total land cover area change ranging from 20935 to 22127 km² over West Africa with variation from one climatic zone to the other.

Changes from (-1.0 to 1.0) \(^\circ\)C (Fig. 13e-h and Fig. 13q-t) aggravate changes ranging from 45039 to 52133 km² of the West African domain as can be seen from land cover changes (2000 − 1999,2018 − 2017). Finally, a change within (-0.5 to 0.5) \(^\circ\)C (Fig. 13i-l) leads to a change in up to 31843 km². The precipitation and climatic water balance changes are similar for each of the periods and within − 50 to 50 mm in most cases with a few areas below and above − 50 and + 50 mm respectively. Within the dry regime (1980 to ~ 2004), most of the land cover changes were experienced when at least 80% of the West African space was dominated by deficits in precipitation and climatic water balance up to -50 mm (Fig. 13u-af). Within the wet regime (2000–2020), most of the region is dominated by an increase (recovery) of rainfall and the climatic water balance of up to about 40 mm, covering at least 35% of the entire West African domain (Fig. 13ag-an). From the foregoing, the plausible climatic conditions leading to significant land cover changes in West Africa can be summarized as follows.

Table 5

Climatic conditions leading to abrupt land cover area changes
Scenario	Condition	T/\(℃\)	\(\varDelta\)T/\(℃\)	P/mm	\(\varDelta\)P/mm	CWB/mm	\(\varDelta\)CWB/mm	\(\varDelta\)Area /km²
Wet	change	-1.0–1.0	2	0 – +50	up to 50	0 – +50	up to 50	45039–52133
	increase	0–1.5	1.5	0 – +50	up to 50	0 – +50	up to 50	20935–22127
	change	-0.5– 0.5	1.0	0 – +50	up to 50	0 – +50	up to 50	31843
Dry	change	-1.0–1.0	2.0	-50–0	up to -50	-50–0	up to -50	45039–52133
	increase	0–1.5	1.5	-50–0	up to -50	-50–0	up to -50	20935–22127
	change	-0.5–0.5	1.0	-50–0	up to -50	-50–0	up to -50	31843

The wet and dry conditions in Table 5 are so presented to emphasize that similar temperature changes with contrasting precipitation and water balance (Fig. 13; 1995 − 1994, 2016 − 2015) effects have similar effects on land surface area change (Fig. 10; 1995, 2016). The results also reveal that a positive increase in temperature everywhere over the region may have less impact on total land cover area change than contrasting changes with a decrease in some regions and increases in others or a change in temperature from cold (hot) conditions to hot (cold) conditions over a region. This is evident in Fig. 13 and explains why a change of 0 to 1.5\(℃\) has less impact on land surface changes (20935–22127 km²) compared to -0.5 to 0.5 \(℃\) changes (31843 km²). A change of -1.0 to 1.0, however, has much more impact on land surface area change than − 0.5 to 0.5.

3.9 Relationship between climate and land cover changes under normal climatic conditions

In Fig. 14 below, we quantify the climatic conditions at normal years to verify that abrupt changes in climatic conditions as asserted above indeed lead to abrupt land cover changes. In general, the temperature change ranged mostly between \(\pm 0.5℃\) covering 50 to 90% under normal climatic conditions. Similarly, changes in precipitation and climatic water balance ranged between − 20 to +20mm covering over 80% of the West African domain. These climatic conditions are well below the conditions for which critical land cover changes were experienced with a total land cover area change of less than 20000 km².

3.10 Climatic risks of West Africa: average climatic change

The Mann-Kendall slope suggests that the climate of West Africa is changing at a rate that varies from − 0.025 to 0.025 per year from one region to the other. For STI the changes are positive (0 to 0.025) everywhere for all the datasets and suggest that temperature is increasing everywhere within West Africa from 1980 to 2020 and also for all seasons (Fig. 15). The SPI and SPEI indices, on the other hand, suggest an increase in precipitation mostly in the Sudan and Sahel zone with little changes in the Guinean and the Sahara zones. Three datasets (CHIRTS/CHIRPS, CRU, TCM) show a similar decrease based on their SPEI values except that the decrease is much greater than seen for the SPI. ERA5-Land however shows significant decreases based on the SPI and SPEI values.

3.11 Climatic risk of West Africa: Occurrence frequency and probability of occurrence

The occurrence frequency and probability of occurrence of the various climatic conditions are shown in Figs. 16 and 17 respectively below. Based on both parameters, the West African region is subjected mainly to a normal climate with an average probability of occurrence of about 0.6 for all the datasets. The occurrence frequencies and probability of occurrence both decrease further with a departure from the normal climate consistent with theoretical values in Tables 3 and 4. We summarize here the probability of occurrence and the occurrence frequency of the climatic conditions for all three climatic indices using boxplots (Fig. 16), where the horizontal lines in Fig. 16b are the theoretical probability of occurrence. The spatial representation of the various indices over West Africa is also shown in Figs. 17 and Fig. 18.

4.1 Spatiotemporal land cover changes

Spatiotemporal land cover change analysis over West Africa reveal changes from the Guinean zone to the Sahara zone. Out of about 74.7 million pixels analyzed per year from 1992 to 2019, only 5,433,127 300m pixels representing 7.28% of the entire West African space have changed. Out of this the 1-time, 2-times, and 3-times changes represent 97.989%, 2.097%, and 0.005% respectively. According to (Defourny et al., 2017; X. Liu, Yu, Sia, et al., 2018) the number of times a pixel changes is an indication of the quality flag such that one-time changes are more reasonable whilst the three times changes represent more unreasonable changes. The fact that over about 98% of the total changes are one-time changes indicates the ESA CCI LC maps can capture changes in West Africa and may be useful for land cover change analysis over the region. The result obtained in this work is comparable to (X. Liu, Yu, Li, et al., 2018) who found 1x changes from 1992 to 2015 to be 98% at the global continental scale. Cotillon and Tappan, 2016 investigating the drivers of land cover change in West Africa noted that the myriad of factors responsible for land cover change may be categorized into human activities and natural factors acting on the earth. Population size and growth on one hand are the major human activities leading to land use land cover change while climate change is the most dynamic natural force affecting land cover change at annual and decadal timescales (Cotillon and Tappan, 2016). Our findings show that human activities to a larger extent and climate to a lesser extent are the most driving factors leading to land use land cover change in West Africa, consistent with the assertions of (Cotillon and Tappan, 2016). Changes from the mid-Sahelian zone to the mid-Saharan zone result from nearly equal contribution from human and natural factors while changes from the Guinean and Sudan zones are mainly human with minimal natural or climatic factors. In the Guinea zone deforestation, slash-and-burn agriculture, population growth, and urbanization are the major human factors driving land cover change. Integration of West Africa into the global economy with investment in the timber and gold industry has led to significant loss in forest areas through logging and mining activities in the Guinean forest countries (Cotillon and Tappan, 2016; Gbedzi et al., 2022). Urban demand for charcoal for cooking is met by tree-falling for charcoal production mainly from the Sudanian zone. It has been estimated that the demand for charcoal in sub-Saharan Africa will continue to rise in the coming decades due to increasing urban demand for charcoal (Mensah et al., 2022; Rose et al., 2022). The climatic impacts on land use land cover change, according to our analysis, are more pronounced in the Sahelian and Saharan zones with relatively harsh climatic conditions and smaller populations compared to the Guinean and Sudanian zones with mild climatic conditions and a large population. The mild climatic conditions of the Guinean and Sudanian zone (mostly bimodal rainfall pattern) enhances rejuvenation of the land cover and less degradation compared to Sahel and Saharan zones exposed to constant flood and drought risks. The contrast in change drivers between Guinean-Sudan and Sahel-Saharan zones is also responsible for the stability of the former compared to the latter in terms of climate change impacts. It follows that losses in the various climatic zones are determined by the relative impacts of the different drivers in the regions aside from the socio-economic and population size and growth. For instance, while the losses in tree cover and grassland in the Guinean and Sudanian zones could be attributed to deforestation and other anthropogenic activities such as agriculture expansion, losses of tree cover in the Sahel and Saharan zone could be more related to harsh climatic conditions. In this work, the spatiotemporal analysis of the land cover change frequency, the change types, the drivers of change, the land cover state, and loss types permits understanding the impacts of regional climate on West African land surface. However, very useful information could be deduced from the plausible impacts of the West African land surface on regional climate. Our findings show that tree-cover loss (essentially forest) and agricultural land losses/conversions constitute the major land cover losses in WA, and also the major impacts that the land surface could have on regional climate. Thus, the most probable pathways through which land use land cover change impacts the climate of West Africa are the conversions involving tree-cover loss and cropland losses in addition to grassland and shrubland losses. With the confidence that the ESA CCI LC maps accurately represent changes in West Africa's land surface, the climate modeling community in West Africa needs to take into consideration conversions related to forest, cropland, shrubland and grassland to accurately simulate the impacts of West African land surface on the regional climate.

4.2 Land cover transitions

The land cover change transitions in the ESA CCI LC maps reveal two major striking pieces of information. On the one hand, cropland, forest, and sparse vegetation including grass and shrubs land cover types exhibit changes in land cover change area and automatically are the land cover types possible in ESA CCI LC maps for investigating the impact of West African land surface on regional climate change. This characteristic is consistent across harmonized classification schemes such as the ESA, IPCC, GLC-Share used for our analysis and implies that the ESA CCI LC maps are specifically designed to capture changes related to these classes. This is also somewhat consistent with Defourny et al. (2017) that the change detection in the CCI maps is according to six IPCC land categories namely cropland, forest, grassland, wetland, settlement, and other land (shrubland, sparse vegetation, bare, water). While this was intended to meet the needs expressed by the climate users to avoid false change detection between LC classes which are semantically close, transitional analysis of the changed pixels shows wetland and urban remains constant and in addition to bare areas and water, even though there are inter-annual changes of these classes in the ESA CCI-LC maps. This piece of information reveals limitations in the ESA CCI LC map's capability to capture changes related to wetland and urban over the region. It could however result from the fact that three out of four of these classes are obtained from external data sources and their area coverage is also small (< 7% in total). For instance, wetland, as used here, is “tree cover, flooded, saline water” based on the global mangrove atlas, “urban areas” relies both on the Global Urban Footprint and on the Global Human Settlement Layer, and waterbodies results from a compilation of static CCI global maps of open water bodies original at 150m resolution, resampled to 300m using an average algorithm (Defourny et al., 2017). The fact that these classes remain constant in transition from 1992 to 2019 implies that the ESA CCI maps have limited applicability in detecting changes in wetland and urban changes over West Africa, and need region-specific evaluation for climate applications as expressed by other studies (Reinhart et al., 2021).

4.3 Annualized land cover change intensities

In this work, both spatiotemporal land cover change analysis and land cover change intensity analysis were used. While spatiotemporal land cover change analysis allows identifying the different drivers of change and change types, the annualized land cover intensities is the unique analysis employed to identify years where possible links between climate variation and land cover changes do exist. Specifically, the annualized land cover change intensities reveal abrupt large changes in some years and relatively smaller changes in other years which allows us to select the specific years and investigate the climatic conditions within those years leading to the different land cover changes. For instance, while a general increase in the land cover area results from the increase in temperature with a corresponding increase in precipitation and climatic water balance in 1994, the change in land cover in 2016 results rather from a decrease in temperature with similar precipitation and climatic water balance increase. Temperature changes are thus critical to the abruptness of increase in land cover change area while regional decreases in precipitation are hardly associated with significant changes in land cover area. The annualized land cover change intensities under normal climate conditions also help to justify why abrupt climatic changes are indeed responsible for abrupt land cover changes.

4.4 Climatic conditions leading to significant land cover changes

Temperature, precipitation, and climatic water balance conditions were linked to abrupt land cover changes using the standardized climatic indices (STI, SPI, and SPEI) at a 12-month timescale. A 12-month timescale was chosen primarily because the land cover dataset for which we intend to establish a relation with climate has a temporal resolution of 1 year making a comparison with climatic indices at an annual timescale reasonable. Consequently, by comparing land-climate information at the same timescales, we delineate those climatic conditions responsible for the observed land cover changes. Within the climate colder regime (1980–2000), most intense land change areas were observed at the peak between two successive colder climate regimes, and for abrupt changes from a hotter climate to a colder climate. Conversely, for a hotter climate, the most significant land cover changes occur at the peaks between two successive hot climatic conditions or a sharp change from cold to hot climate. These climatic-land cover change conditions can be summarized using the fundamental rules of calculus, where the peak conditions are turning points where the differential (slope) of the climatic indices equals zero irrespective of the climate episode (hot or cold). On the other hand, migration from hot (cold) to cold (hot) is designated by events where the slope of the climatic indices is negative (positive). In this regard, the slope of a Mann-Kendall trend test is suggested for delineating the average climatic condition a region is exposed to in a long term. Alternatively, a threshold-based probability of occurrence of STI, SPI, and SPEI is suggested to characterize the relative exposure of different areas to different STIs, SPIs, and SPEIs. Lastly, the climatic conditions leading to significant land cover changes in specific years could be delineated using the changes in climatic conditions before and during those years. The last approach used in this work shows that a change in -1.0 to 1.0\(^\circ\)C with about 50 mm of changes in precipitation and 40-50mm climatic water balance could lead to annual land cover change intensity of 45039 to 52133 km² while a change in up to 0 to + 1.5\(^\circ\) could aggravate 20935 to 22127 km² with the same precipitation and climatic water balance conditions. A change of -0.5 to 0.5\(^\circ\) with similar changes in precipitation and climatic water balance could result in about 31834 km² change area. Finally, the probability of occurrence of the various climatic conditions found for West Africa using the four different datasets is consistent with the well-known values in literature, with the normal climate at approximately 0.6 and decreasing with departure from the normal climate.

The climate-land interaction is an active field of research and forms the basis for improving climate models and land surface schemes for regional studies. This study adopts pixel-wise analysis of ESA CCI LC maps from 1992 to 2019 and the standardized temperature, standardized precipitation, and standardized precipitation evapotranspiration indices to establish a relationship between land cover change and climatic changes over West Africa. While the ESA CCI LC maps prove efficient for capturing changes related to land cover sustainability development indicators such as land cover change times, degradation/improvement/stable issues, and lose types, it is limited in their ability to capture changes related to wetlands and urban, in addition to bare and water which may be of interest to other climate-related studies. The ESA CCI maps essentially capture changes related to cropland, forest, grassland, and shrubland conversions which are mostly human-induced changes, with natural changes predominant somewhat in the Sahel and Sahara zones. Spatiotemporal analysis of climatic indices and annualized land change intensities shows that the main climatic impact on land cover changes in West Africa results mostly from abrupt changes from one climatic condition to another or a change in temperature with mostly increases in precipitation and climatic water balance. Conversely, the high conversion of cropland, forest, shrubland, and grassland is the main anthropogenic forcing of land use land cover change on the West African climate. With abrupt changes in the climate, the land cover changes which is normally below 20000 km² in a typical year could be twice as much. The research outcome is not only useful for the climate modeling community as historical evidence of land use impact on climate and vice versa, which provides a basis for re-evaluating climate-land inputs into regional model simulations, but also useful in several areas. Firstly, this research informs on regional degradation extent which requires the need for policy interventions to reduce the degradation rate of the Sahel and Sahara zones with harsh climatic conditions. Secondly, a critical review of policies that promotes mining at the expense of forest and tree cover in the Guinea zone is needed for sustainable management of forest and tree resources. Thirdly, policy-driven technology transfer of efficient charcoal production could be taught to the populace in the Sudanian zones to promote efficient, selective, and controlled logging of trees for charcoal in the Sudanian region. The cumulative effect of the above-mentioned will ensure that the impact of abrupt changes in the climatic condition which is uncontrollable in most cases does not have unprecedented impacts on the West African land surface.

Funding

The authors acknowledge with thanks the scholarship and financial support provided by BMBF, the Federal Ministry of Education and Research of Germany, and the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL).

Competing Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Authors contribution

All authors contributed to the study's conceptualization and design. Material preparation, data preparation, and analysis were performed by Eric Mensah Mortey. The first draft of the manuscript was written by Eric Mensah Mortey and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Statements & Declaration

Funding

Competing Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Authors contribution

All authors contributed to the study's conception and design. Material preparation, data preparation, and analysis were performed by Eric Mensah Mortey. The first draft of the manuscript was written by Eric Mensah Mortey and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Availability of data and material

Multiple datasets from a variety of sources are employed in this study to establish the relationship between climate and land. The 300m ESA CCI land cover data was obtained from http://maps.elie.ucl.ac.be/CCI/viewer/ in TIFF format and https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) in NetCDF format.

The CRU datasets used is at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts 4.05/cruts.2103051243.v4.05/. The ERA5-Land data was obtained from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly means?tab=overview/. The GLEAM datasets is also available also available at sftp://hydras.ugent.be. The CHIRPS precipitation datastes is available at https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p05/. The CHIRTS temperature is available at http://data.chc.ucsb.edu/products/CHIRTSdaily/v1.0/africa_netcdf_p05. The TerraClimate data is available at http://thredds.northwestknowledge.net:8080/thredds/terraclimate_aggregated.html.

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5(170191), 1–12. https://doi.org/10.1038/sdata.2017.191

Achugbu, I. C., Olufayo, A. A., Balogun, I. A., Adefisan, E. A., Dudhia, J., & Naabil, E. (2021). Modeling the spatiotemporal response of dew point temperature, air temperature and rainfall to land use land cover change over West Africa. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-021-01094-8

Akpoti, K., Antwi, E., & Kabo-bah, A. (2016). Impacts of Rainfall Variability, Land Use and Land Cover Change on Stream Flow of the Black Volta Basin, West Africa. Hydrology, 3(3), 26. https://doi.org/10.3390/hydrology3030026

Alamdari, N., Claggett, P., Sample, D. J., & Easton, Z. M. (2022). Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed. Journal of Cleaner Production, 330(November 2021), 129953. https://doi.org/10.1016/j.jclepro.2021.129953

Amir, S., Griffiths, G., & Lukac, M. (2021). Land use and climate change interaction triggers contrasting trajectories of biological invasion. Ecological Indicators, 120(September 2020), 106936. https://doi.org/10.1016/j.ecolind.2020.106936

Annor, T., Lamptey, B., & Washington, R. (2022). Assessment of the unified model in reproducing West African precipitation and temperature climatology. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-022-03973-1

Barras, A. G., & Braunisch, V. (2021). Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change. Diversity and Distributions, 27(6), 989–1004. https://doi.org/10.1111/ddi.13247

Boone, A. A., Xue, Y., De Sales, F., Comer, R. E., Hagos, S., Mahanama, S., Schiro, K., Song, G., Wang, G., Li, S., & Mechoso, C. R. (2016). The regional impact of Land-Use Land-cover Change (LULCC) over West Africa from an ensemble of global climate models under the auspices of the WAMME2 project. Climate Dynamics, 47(11), 3547–3573. https://doi.org/10.1007/s00382-016-3252-y

Cao, F., & Dan, L. (2021). The impact of land use and land cover change on regional climate over East Asia during 1980 – 2010 using a coupled model. Theoretical and Applied Climatology, 145(1), 549–565. https://doi.org/https://doi.org/10.1007/s00704-021-03629-6

Casazza, G., Malfatti, F., Brunetti, M., Simonetti, V., & Mathews, A. S. (2020). Interactions between land use , pathogens , and climate change in the Monte Pisano , Italy 1850 – 2000. Landscape Ecology, 36(2), 601–616. https://doi.org/10.1007/s10980-020-01152-z

Chilukoti, N., & Xue, Y. (2021). An assessment of potential climate impact during 1948–2010 using historical land use land cover change maps. International Journal of Climatology, 41(1), 295–315. https://doi.org/10.1002/joc.6621

Cotillon, S. E., & Tappan, G. G. (2016). Landscapes of West Africa: A window on a changing world.

Dale, V. H. (1997). The relationship between land‐use change and climate change. Ecological Applications, 7(3), 753–769.

Danandeh Mehr, A., & Vaheddoost, B. (2020). Identification of the trends associated with the SPI and SPEI indices across Ankara, Turkey. Theoretical and Applied Climatology, 139(3–4), 1531–1542. https://doi.org/10.1007/s00704-019-03071-9

Defourny, P., Bontemps, S., Lamarche, C., Brockmann, C., Boettcher, M., Wevers, J., & Kirches, G. (2017). Land cover CCI: product user guide version 2.0. http://maps.elie.ucl.ac.be/CCI/viewe r

Deng, X., Zhan, J., & Su, H. (2014). Land Use Impacts on Climate. Springer. https://doi.org/10.1007/978-3-642-54876-5

Edwards, D. C., & McKee, T. B. (1997). Characteristics of 20th Century drought in the United States at multiple time scales. 298(0704).

Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R., Malyshev, S., Santanello, J. A., & Shevliakova, E. (2017). The impact of anthropogenic land use and land cover change on regional climate extremes. Nature Communications, 8(1), 1–9. https://doi.org/10.1038/s41467-017-01038-w

Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. Scientific Data, 2(150066), 1–21. https://doi.org/10.1038/sdata.2015.66

Gabiri, G., Diekkrüger, B., Näschen, K., Leemhuis, C., Linden, R. van der, Majaliwa, J.-G. M., & Obando, J. A. (2020). Impact of Climate and Land Use / Land Cover Change on the Water Resources of a Tropical Inland Valley. Climate, 8(7), 83.

Gabiri, G., Leemhuis, C., Diekkrüger, B., Näschen, K., Steinbach, S., & Thonfeld, F. (2019). Modelling the impact of land use management on water resources in a tropical inland valley catchment of central Uganda, East Africa. Science of the Total Environment, 653, 1052–1066. https://doi.org/10.1016/j.scitotenv.2018.10.430

Gao, J., & Liu, Y. (2011). Climate warming and land use change in Heilongjiang Province, Northeast China. Applied Geography, 31(2), 476–482. https://doi.org/10.1016/j.apgeog.2010.11.005

Gbedzi, D. D., Ofosu, E. A., Mortey, E. M., Obiri-Yeboah, A., Nyantakyi, E. K., Siabi, E. K., Abdallah, F., Domfeh, M. K., & Amankwah-Minkah, A. (2022). Impact of mining on land use land cover change and water quality in the Asutifi North District of Ghana, West Africa. Environmental Challenges, 6(August 2021). https://doi.org/10.1016/j.envc.2022.100441

Guidigan, M. L. G., Sanou, C. L., Ragatoa, D. S., Fafa, C. O., & Mishra, V. N. (2019). Assessing Land Use/Land Cover Dynamic and Its Impact in Benin Republic Using Land Change Model and CCI-LC Products. Earth Systems and Environment, 3(1), 127–137. https://doi.org/10.1007/s41748-018-0083-5

Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1), 1–18. https://doi.org/10.1038/s41597-020-0453-3

Hou, S. shuang, Yu, L. fei, Yan, W., Gu, S. hong, & Gu, X. ping. (2021). The effects of land use changes on climate warming by source-sink theory. Arabian Journal of Geosciences, 14(7), 1–17. https://doi.org/10.1007/s12517-021-06678-3

Hung, C. J., James, L. A., Carbone, G. J., & Williams, J. M. (2020). Impacts of combined land-use and climate change on streamflow in two nested catchments in the Southeastern United States. Ecological Engineering, 143(November 2019), 105665. https://doi.org/10.1016/j.ecoleng.2019.105665

Idrissou, M., Diekkrüger, B., Tischbein, B., Hipt, F. Op De, Näschen, K., Pom, T., Yira, Y., & Ibrahim, B. (2022). Modeling the Impact of Climate and Land Use / Land Cover Change on Water Availability in an Inland Valley Catchment in Burkina Faso. Hydrology, 9(12). https://doi.org/https:// doi.org/10.3390/hydrology9010012

International Conservation. (2018). Trends.Earth (Vol. 2018, pp. PA23A-10).

Jain, S., Roy, S. B., Panda, J., & Rath, S. S. (2021). Modeling of land-use and land-cover change impact on summertime near-surface temperature variability over the Delhi–Mumbai Industrial Corridor. Modeling Earth Systems and Environment, 7(2), 1309–1319. https://doi.org/10.1007/s40808-020-00959-8

Jiang, L., & Yu, L. (2019). Analyzing land use intensity changes within and outside protected areas using ESA CCI-LC datasets. Global Ecology and Conservation, 20(July 2018). https://doi.org/10.1016/j.gecco.2019.e00789

Kalnay, E., Cai, M., Karl, T. R., Williams, C. N., Young, P. J., Wendland, W. M., & Easterling, D. R. (2003). United States Historical Climatology Network (US HCN) Monthly Temperature and Precipitation Data (Carbon Dioxide Inform. J. Climate Appl. Meteorol, 423(4500), 1265–1270. www.nature.com/nature

Kocur-bera, K. (2018). Impact of land use on climate change. Engineering for Rural Development-International Scientific Conference, 457–462. https://doi.org/10.22616/ERDev2018.17.N036

Li, W., Macbean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., & Peng, S. (2018). Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps ( 1992 – 2015 ). Earth System Science Data, 10, 219–234. https://doi.org/https://doi.org/10.5194/essd-10-219-2018

Liu, C., Yang, C., Yang, Q., & Wang, J. (2021). Spatiotemporal drought analysis by the standardized precipitation index ( SPI ) and standardized precipitation evapotranspiration index ( SPEI ) in Sichuan Province ,. Scientific Reports, 11(1280), 1–14. https://doi.org/10.1038/s41598-020-80527-3

Liu, J., You, Y., Li, J., Sitch, S., Gu, X., Nabel, J. E. M. S., Lombardozzi, D., Luo, M., Feng, X., Arneth, A., Jain, A. K., Friedlingstein, P., Tian, H., Poulter, B., & Kong, D. (2021). Response of global land evapotranspiration to climate change , elevated CO 2 , and land use change. Agricultural and Forest Meteorology, 311(August), 108663. https://doi.org/10.1016/j.agrformet.2021.108663

Liu, S., Liu, X., Yu, L., Wang, Y., Zhang, G. J., Gong, P., Huang, W., & Wang, B. (2021). Climate response to introduction of the ESA CCI land cover data to the NCAR CESM. Climate Dynamics, 56(11), 4109–4127. https://doi.org/10.1007/s00382-021-05690-3

Liu, X., Yu, L., Li, W., Peng, D., Zhong, L., Li, L., Lu, H., Yu, C., & Gong, P. (2018). Comparison of country-level cropland areas between ESA-CCI land cover maps and FAOSTAT data. International Journal of Remote Sensing, 39(20), 6631–6645. https://doi.org/10.1080/01431161.2018.1465613

Liu, X., Yu, L., Sia, Y., Zhang, C., Lu, H., Yu, C., & Gong, P. (2018). Identifying patterns and hotspots of global land cover transitions using the ESA CCI land cover dataset. Remote Sensing Letters, 9(10), 972–981. https://doi.org/10.1080/2150704X.2018.1500070

Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., & Verhoest, N. E. C. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183. https://doi.org/10.1088/1755-1315/5

Mensah, K. E., Damnyag, L., & Kwabena, N. S. (2022). Analysis of charcoal production with recent developments in Sub-Sahara Africa: a review. African Geographical Review, 41(1), 35–55. https://doi.org/10.1080/19376812.2020.1846133

Molotoks, A., Smith, P., & Dawson, T. P. (2021). Impacts of land use , population , and climate change on global food security. Food and Energy Security, 10(1), 1–20. https://doi.org/10.1002/fes3.261

Mortey, E. M., Kouassi, K. L., Diedhiou, A., Anquetin, S., Genoud, M., Hingray, B., Guy, D., & Kouame, M. (2019). Sustainable Hydroelectric Dam Management in the Context of Climate Change : Case of the Taabo Dam in C ô te D ’ Ivoire , West Africa. Sustainability, 11(18), 32. https://doi.org/10.3390/su11184846

Muñoz-sabater, J., Dutra, E., Agustí-panareda, A., Albergel, C., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., & Rodríguez-fernández, N. J. (2021). ERA5-Land : a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349–4383.

Nyatuame, M., Amekudzi, L. K., & Agodzo, S. K. (2020). Assessing the land use/land cover and climate change impact on water balance on Tordzie watershed. Remote Sensing Applications: Society and Environment, 20(August), 100381. https://doi.org/10.1016/j.rsase.2020.100381

Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., & Funk, C. (2020). A pan-African high-resolution drought index dataset. Earth System Science Data, 12(1), 753–769. https://doi.org/https://doi.org/10.5194/essd-12-753-2020

Pipitpukdee, S., Attavanich, W., & Bejranonda, S. (2020). Impact of Climate Change on Land Use , Yield and Production of Cassava in Thailand. Agriculture, 10(9), 402.

Pramudya, Y., Dewi, W. S., & Sukoco, T. A. (2016). Remote Sensing for estimating agricultural land use change as the impact of climate change. Proc. of SPIE, 987720(May 2016). https://doi.org/10.1117/12.2223878

Préau, C., Bertrand, R., Sellier, Y., Grandjean, F., & Isselin‐Nondedeu, F. (2021). Climate change would prevail over land use change in shaping the future distribution of Triturus marmoratus in. Animal Conservation, 1–12. https://doi.org/10.1111/acv.12733

Reinhart, V., Fonte, C. C., Hoffmann, P., Bechtel, B., Rechid, D., & Boehner, J. (2021). Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective. International Journal of Applied Earth Observation and Geoinformation, 94(April 2020), 102221. https://doi.org/10.1016/j.jag.2020.102221

Rose, J., Bensch, G., Munyehirwe, A., & Peters, J. (2022). The forgotten coal: Charcoal demand in sub-Saharan Africa. World Development Perspectives, 25, 100401. https://doi.org/http://dx.doi.org/10.4419/96973097 This

Ross, E. R., & Randhir, T. O. (2022). Effects of climate and land use changes on water quantity and quality of coastal watersheds of Narragansett Bay. Science of the Total Environment, 807, 151082. https://doi.org/10.1016/j.scitotenv.2021.151082

Sawadogo, W., Abiodun, B. J., & Okogbue, E. C. (2019). Projected changes in wind energy potential over West Africa under the global warming of 1.5 °C and above. Theoretical and Applied Climatology, 138(1–2), 321–333. https://doi.org/10.1007/s00704-019-02826-8

Schulte to Bühne, H., Tobias, J. A., Durant, S. M., & Pettorelli, N. (2021). Improving Predictions of Climate Change–Land Use Change Interactions. In Trends in Ecology and Evolution (Vol. 36, Issue 1, pp. 29–38). Elsevier Ltd. https://doi.org/10.1016/j.tree.2020.08.019

Selwood, K. E., Mcgeoch, M. A., & Nally, R. Mac. (2014). The effects of climate change and land-use change on demographic rates and population viability. Biological Reviews, 90(3), 837–853. https://doi.org/10.1111/brv.12136

Sims, N. C., England, J. R., Newnham, G. J., Alexander, S., Green, C., Minelli, S., & Held, A. (2019). Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals. Environmental Science and Policy, 92(October), 349–355. https://doi.org/10.1016/j.envsci.2018.10.014

Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., & Michaelides, K. (2021). Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Scientific Data, 8(1), 1–13. https://doi.org/10.1038/s41597-021-01003-9

Srinivasan, U., & Wilcove, D. S. (2021). Interactive impacts of climate change and land-use change on the demography of montane birds. Ecology, 102(1). https://doi.org/10.1002/ecy.3223

Sylla, M. B., Pal, J. S., Wang, G. L., & Lawrence, P. J. (2016). Impact of land cover characterization on regional climate modeling over West Africa. Climate Dynamics, 46(1–2), 637–650. https://doi.org/10.1007/s00382-015-2603-4

Tirupathi, C., & Shashidhar, T. (2020). Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios. Science of the Total Environment, 721, 137736. https://doi.org/10.1016/j.scitotenv.2020.137736

Tsendbazar, N. E., Bruin, S. de, Fritz, S., & Herold, M. (2015). Spatial accuracy assessment and integration of global land cover datasets. Remote Sensing, 7(12), 15804–15821. https://doi.org/10.3390/rs71215804

Tu, J. (2009). Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA. Journal of Hydrology, 379(3–4), 268–283. https://doi.org/10.1016/j.jhydrol.2009.10.009

Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., & Grace, K. (2020). Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set. Scientific Data, 7(1), 1–14. https://doi.org/10.1038/s41597-020-00643-7

Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A Multiscalar Drought Index Sensitive to Global Warming : The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1

Wajid, M., Safi, A., Siddiqui, S., & Tariq, A. (2020). Impact of Climate Change on Land use / Land cover of Chakwal District (Vol. 11, Issue 2).

Yildirim, G., & Rahman, A. (2022). Homogeneity and trend analysis of rainfall and droughts over Southeast Australia. Natural Hazards. https://doi.org/10.1007/s11069-022-05243-9

Zhou, Q., Li, B., Kurban, A., Kowloon, H. K., & Kong, H. (2008). Trajectory analysis of land cover change in arid environment of China. International Journal of Remote Sensing, 29(4), 1093–1107. https://doi.org/10.1080/01431160701355256

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Relationship between climate and land use land cover change over West Africa

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Abstract

Figures

1. Introduction

2. Data And Methods

2.1. Study area

2.2 Data

2.2.1 Land cover data

2.2.2 Climate data

2.3 Methods

2.3.1 Land cover change analysis

2.4 Climate indices

2.4.1 Standardized Precipitation Index (SPI)

2.4.2 Standardized Precipitation Evapotranspiration Index (SPEI)

2.4.3 Standardized Temperature Index (STI)

2.5 Linking land-climate changes

2.6 Average climate rate of change

2.7 Climatic indices occurrence frequency and probability of occurrence

3 Results

3.1 Latitudinal change occurrence frequency

3.2 Land cover change drivers

3.3 Changed state

3.4 Land cover loss type

3.5 Land cover transition

3.6 Annualized changes in land cover over West Africa

3.7 Climatic conditions leading to significant land cover change area

3.8 Relationship between climate and land use and cover change: quantifying the changes

3.9 Relationship between climate and land cover changes under normal climatic conditions

3.10 Climatic risks of West Africa: average climatic change

4. Discussion

4.1 Spatiotemporal land cover changes

4.2 Land cover transitions

4.3 Annualized land cover change intensities

4.4 Climatic conditions leading to significant land cover changes

5 Conclusion

Declarations

References

Supplementary Files

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