TABLE OF CONTENT
- Introduction
1.1 Aim and objectives of the study
2.0 Description of the study area
2.1 Vegetation types and land uses
2.2 Rainfall pattern and NDVI
2.3 Lake Chad basin and degradation
2.4 SDG’s
3.0 Methodology
3.1 Data sources
3.2 NDVI
3.3 ESA CCI
3.4 LULC classification and changes analysis
4.0 Results and Discussion
4.1 Land use land cover spatiotemporal changes analysis
4.2 Change dynamic of the surface area of lake Chad basin
4.3 Impact of climate variables on lake Chad surface water
4.4 Lake Chad basin surface water against climate variables
4.5 Determination of the response of lake Chad water change
to vegetable / NDVI
4.6 MDA’s
References
Abstract
Lake Chad, in the Sahelian zone of west-central Africa bordered by four countries Chad, Nigeria, Niger and Cameroon, provides food and water to approximately 50 million people whilst supporting a unique ecosystem and biodiversity. In the past decades, it has grown to become a symbol of current climate change dispute held up by its dramatic shrinkage in the 1980s. Despite a partial recovery in response to increased precipitation recorded sometimes in the 1990s, the lake is still facing major threats and its contemporary variability under climate change remains highly uncertain.
Subsequent to the International Conference on Lake Chad held in Nigeria, 2018; a roadmap was developed. It was agreed that a 3% – 8% Inter-Basin Water Transfer (IBWT) from the Ubangi River and the Congo River Basin no longer an option but a necessity as the “preferred option” to save Lake Chad. To achieve this, a focus on temporal variation of vegetation with relation to precipitation in the basin needs to be properly accessed.
This study assessed and analyzed the relationship between climatic factors and vegetation alongside NDVI, to evaluate productivity over the Lake Chad. The response of climatic effects to inter-annual integral variation of NDVI was analyzed over a period from 2001 to 2020.
- INTRODUCTION
The Lake Chad Basin is situated in the eastern part of the Sahel region at the southern edge of the Sahara Desert. The basin covers 8% of the surface area of Africa, shared between the countries of Algeria, Cameroon, Central African Republic (CAR), Chad, Libya, Niger, Nigeria, and Sudan (UNEP, 2004). It is one of the largest sedimentary groundwater basins in Africa extending over an area of about 2,381,000 km² and an estimated population of about 47 million in 2013. The Chad basin is the largest area of the inland drainage basin in Africa covering an area of 2.34 to 2.5 km2 (Oteze & Fayose, 1988; Leblanc, 2002; UNEP, 2004).
The climate is classified in four Sub-types: Saharan climate is characterized by less than 100 mm of rainfall per year; Sahelian-Saharan climate with an average annual rainfall ranging from 100 to 400 mm; Sahelian-Sudanese climate, more wet with an average annual rainfall ranging from 400 to 600 mm; and the Sudanese-Guinean climate, with an average annual rainfall in the area ranging from 600 to 1500mm.
Generally, the region is characterized by high temperatures throughout the year with consequent low humidity except during the rainy season from June to September. Intense solar radiation and strong winds lead to a high annual potential evapotranspiration of around 2,200mm for Central Chad (Carmouze, 1976). The basin has been classified into different swamp regions, known as the Grand Yaéré in Extreme north of Cameroon (Ketchemen, 1992), Lake Chad, Lake Fiti, Massénya and Salamat to the south-east, and Komadougou-Yobé to the North-east of Nigeria
The basin is named for its most prominent feature, Lake Chad. The Lake Chad’s water supply is primarily from the Chari-Logone River (330 000 km2), which provides approximately 90% to the southern pool and the remaining 10% with the Komadugu-Yobe River (120 000 km2) contributing to the northern pool.
The water balance of the Lake is highly variable resulting in fluctuating open surface waters that have exhibited dramatic expansion and contraction over geologic and recent history. In the last decades the open water surface has reduced from approximately 25 000 km2 circa 1963, to less than 2000 km2 in the early 1990s (Olivry et al. 1996, Grove 1996, Coe & Foley 2001). Although, for the 1998-2004 period the Lake water surface has increased to about 3,000 km2 following increased rainfall.
Vegetation responds simply and subtly to weather change and in this region the vegetation is the key component of the earth’s terrestrial ecosystems and plays a major role in soil, energy exchange, and biogeochemical cycles on earth’s surface (Liu et al., 2018); (Ning et al., 2015). Precipitation in the Lake Chad basin is highly variable both spatially and temporally, and experiences periods of prolonged drought such as that which affected the region in the 70’s and 80’s (Boyd et al., 2013); Nicholson, 2013). However, the spatiotemporal variability of vegetation coverage is of great importance in the future, exploring the internal relationship between vegetation and climate change, exposing the process of evolution of regional environmental conditions, and forecasting future trends in growth (Zhang et al., 2018); (Chauhan et al., 2020)
Traditionally, people in the basin have grown rainfed crops, like millets, potato, onions, and groundnut, on areas adjacent to the lake and rivers, likewise, sorghum and paddy are grown on areas that got flooded during the monsoon). This indicates clear signs of seasonality and inter-annual change (Chuai et al., 2013); (Cui & Shi, 2010)) and is a touchy indicator of worldwide climate change (Weiss et al., 2004). Nowadays, diminished soil conditions in the area affect particularly vegetation growth, in which the water system, as well as vegetation, remains vulnerable (Froend & Sommer, 2010).
- Aim and Objective of the Study
This study aims to use geospatial techniques to analyze change in surface water of Lake Chad Basin as it responds to climate and vegetation impacts.
Objectives:
- Produce Land Use Land Cover for Lake Chad Basin using recent imagery
- Analyze the change dynamics of the surface area of Lake Chad Basin
- To determine the impact of climate variables on Lake Chad surface water
- To determine the response of Lake Chad water change to vegetation/NDVI
- Description of Study Area
The Lake Chad is located in the Sahelian zone of west-central Africa at the conjunction of Chad, Cameroon, Nigeria, and Niger. The Lake Chad Basin extends between Latitudes 60N and 240N; Longitudes 80E and 240E. It stretches over an area of 2,400,000Km2. It is a shallow lake whose average depth is about 3m, and it is situated in an interior basin. In Nigeria, two sub-basins drain into the lake: the Yedseram/Ngadda sub-basin to the south; and the Hadejia/Jama’are-Komadougou/Yobe sub-basin to the north.
Figure 1: Study Area Map
- Vegetation Types and Land Uses
There is a large contrast in the Vegetation cover of the Lake Chad Basin from the dry North to the tropical South. It is broadly categorized under the three regions of the northern Sahara zone, the central Sahel zone, and the southern Sudan zone. The northern part of the basin which has the Sahara Desert and which borders Libya and the volcanic massif of Tibesti forms part of the northern zone.
Vegetation is dominantly tropical in the southern zone. However, a large area of the basin is made of desert dunes where fringes of xerophytic scrubland are noted. The transition zone that lies between the southern Sahel and northern Sudan–Guinea is a major seasonal wetland. The Sudan Savanna zone mostly consists of Sudanese woodland with intermittent vegetation of edaphic grassland and acacia.
The well-drained soils of the area once supported areas of dense woodlands with ebony and kapok trees, but this has declined due to soil erosion and degradation. Vegetation found in the area includes acacias, baobab, desert date, palms, African myrrh, and Indian jujube. Vegetation plays an irreplaceable function in preserving climate stability, controlling the carbon balance, and rising global greenhouse gases (GHGs), connecting soil, atmosphere, and moisture (Chuai et al., 2013); (Kumar et al., 2017) (Kumar et al., 2018); (Kumar & Kumar, 2020).
However, vegetation is prone to climate change. The dynamics of vegetation and their responses to climate change is recognized as one of the main issues of environmental change in the terrestrial ecosystem. With progress in remote sensing technologies, more information can be obtained from multispectral and multi-dated remote sensing datasets that provide advanced methods for investigating vegetation growth and inter-annual and seasonal variations (Ding et al., 2007)
- Rainfall Patternand NDVI
The region experienced higher rainfall during 1950s–1960s (wet period), which gradually entered a dry regime with frequent drought between 1970s and 1990s (dry period). Afterwards, rainfall began to recover at a very slow rate from the 1990s onward (recovery period; (Caminade & Terray, 2010); (Maharana et al., 2018). These characteristics have been reported from the analysis of long-term rain-gauge, observational and re-analysis data sets (Le Barbé et al., 2002; Maharana et al., 2018). Charney et al. (1977) found that the feedback between land surface conditions (soil humidity, vegetation, and albedo) and atmospheric radiation equilibrium affects the regional precipitation.
The decadal rainfall variability was attributed to the interaction of Hadley and Walker cells over Africa at decadal frequency through anomalous north-south displacement of the near-equatorial trough (Jury, 2009). This variability was attributed to global warming (Paeth & Hense, 2004) and vegetation feedback processes (Charney et al., 1977; Zeng, 1999). There are three climatic zones: hyper-arid in the north, through semi-arid in the central parts, to subtropical in the south (FOA 2009). Most of the studies in this region have focused on the Sahel, but LCB is located at the transition Sahelo–Sudanian zone. As such, it responds to both dry climatic forcing (harmattan wind) (Knippertz and Fink 2008) from the north and wet forcing from the south (West African monsoon).
On the contrary, Niel et al. (2005) reported a significant decrease in annual rainfall in the central part of the basin by analysing a longer rainfall record from rain gauges covering the period 1950–2002. Coe and Foley (2001) suggested that climate variability controls the interannual fluctuations of the water inflow in the Lake Chad basin. Many studies have been carried out to understand the intra-seasonal variability of the West African monsoon (WAM; N’Datchoh et al., 2018; Poan et al., 2016; Roehrig et al., 2013) because of its huge socio-economic impact, particularly over lake chad region.
To date, there have been many research achievements regarding analysis of long-term changes in vegetation driven by human activities and climate factors. Studies on the use of the normalized difference vegetation index (NDVI) to monitor how vegetation responds to changes in climatic condition in Africa include those of Nicholson and Farrar (1994) and Anyamba and Eastman (1996). (Hoerling et al. 2006; Cook 2008).
- Lake Chad Basin Degradation
Over the past half century, Lake Chad has receded drastically due to various environmental pressures, from climate-induced desertification to human-led stream-flow modification. Exemplifying the disproportionate effects of global climate change, the lake’s recession increases water stress within a highly drought prone region.(WFP, 2016). Now reduced to a fraction of its original surface area, the “disappearing lake” faces serious preservation issues as land revealed by the receding shores is being claimed as farmland or new settlements to accommodate rapidly expanding demographic growth in one of the poorest regions of the world (WFP,2016).
Natural rainfall variability, unsustainable water use, and climate change continue contributing to the drying of the lake, which influences the shrinkage of the water.(Taguem Fah, 2007) Environmental challenges, particularly biodiversity loss are further obstacles to human development and food security in the basin.
Also, the humanitarian crisis puts extra pressure on limited resources in the face of severe scarcity and chaos from the onset of regional conflict. Hence, environmental degradation appears both self-perpetuating and inevitable (Meeren et al., 1980). However, a more recent study by (Pham-Duc et al., 2020) using a multi-satellite approach indicated that the only decrease observed was a slight one around the northern pool. Asides that, they found out that the Lake Chad is not shrinking and there is a seasonal recovery of its volume and surface water extent.
- SDG 6: Ensure availability and Sustainable Management of
Water and Sanitation for all
Substantially increase water use efficiency across all sectors and ensure sustainable withdrawal and supply of fresh water to address water scarcity and substantially reduce the number of people suffering from water scarcity.
6.1 By 2030, achieve universal and equitable access to safe and affordable drinking water for all
6.2 By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations
6.3 By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally
6.4 By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity
6.5 By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate
6.6 By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes
6.A By 2030, expand international cooperation and capacity-building support to developing countries in water- and sanitation-related activities and programmes, including water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies
6.B Support and strengthen the participation of local communities in improving water and sanitation management.
- METHODOLOGY
- Data Sources
Spatial precipitation data gotten from TAMSAT portal (www.tamsat.org.uk) for the period of 2001 to 2020 was used as the main data in this research over the Lake Chad basin.
Monthly and seasonal rainfall data was downloaded for the time series 2001 – 2020 over the entire Lake Chad Basin. The spatial data has an advantage over the rain gauge data in that the missing data are automatically filled and ready for use thereby reducing the error possibilities. The monthly, annual, and seasonal time series for the Lake Chad Basin was analyzed to identify rainfall variability for the period.
- NDVI
Normalized Difference Vegetation Index (NDVI) computed using information from the red and near infrared portions of the electromagnetic spectrum. Bi-weekly products from MODIS are used to process annual integrals of NDVI. NDVI trend analysis was downloaded using the Trends.Earth plugin. Trends.Earth performed the analysis using the NDVI, Soil Moisture, Precipitation and Evapotranspiration Dataset as listed in the data sources Error! Reference source not found. and Table 1.
Table 1: Dataset for Land Use
| Variable | Sensor/ Dataset | Temporal | Spatial | Extent | |
| 1 | NDVI | MOD13Q1-coll6 | 2001- 2020 | 250m | Basin Extent |
| 2 | Land Use Classes | ESA CCI | 2001- 2020 | 300m | Basin Extent |
- ESA CCI
The CCI-LC project delivers consistent global LC maps at 300m spatial resolution on an annual basis from 2001 to 2020. A key aspect of the CCI-LC maps consists in their consistency over time. As a result, the set of annual maps are not produced independently but they are derived from a unique baseline LC map which is generated. Independently from this baseline, LC changes are detected at 1 km based on the AVHRR time series, SPOT-VGT time series between and PROBA-V data. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 2001 to 2020.
Table 1: Dataset for Climate Variables
| Variable | Sensor/ Dataset | Temporal | Spatial | Extent | |
| 1 | Precipitation/ Rainfall | CHIRPS/ TAMSAT | 2001- 2020 | 250m | Basin Extent |
| 2 | Evapotranspiration | FLDAS (Climate Engine) | 2001- 2020 | 1km | Basin Extent |
| 3 | Surface Runoff | FLDAS (Climate Engine) | 2001- 2020 | 1km | Basin Extent |
| 4 | Surface Soil Moisture | FLDAS (Climate Engine) | 2001- 2020 | 1km | Basin Extent |
3.4 LULC Classification and Change Analysis
To assess changes in land cover, ESA CCI Land Cover maps covering the study area for the baseline and target years were used. The Land cover maps was reclassified to the 7 land cover classes required for UNCCD reporting (forest, grassland, cropland, wetland, artificial area, bare land, and water). Land cover transition analysis to identify which Land Use/Cover class remained/changed was evaluated.
Table 3: Land Use Land Use Reclassification Model Table 4:
| ESA CCI-LC | IPCC |
| Tree broad leaved evergreen, Tree broad leaved deciduous, Tree needle leaved evergreen, Tree needle leaved deciduous, Tree mixed leaf type, Mosaic tree, shrub/ herbaceous cover | Forest Land |
| Mosaic natural vegetation/ cropland, Mosaic herbaceous cover/ tree, shrub, Shrublands, Grassland, Lichens and mosses, Sparse vegetation | Grassland |
| Cropland rain fed, Cropland, irrigated or post-flooding, Mosaic cropland / natural vegetation | Cropland |
| Tree cover, flooded, saline water, Shrub, or herbaceous cover, flooded
Tree flooded, fresh water |
Wetlands |
| Urban areas | Settlements |
| Other land Bare soil, Snow and Glacier, Bare areas, Permanent snow, and ice Water Bodies Water bodies | Other land |
Land Cover aggregation method
| Land Cover Class | Value |
| Forest Land/ Tree Covered | 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100 |
| Grassland | 40, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153 |
| Cropland | 10, 11, 12, 20, 30 |
| Wetlands | 160, 170, 180 |
| Settlements / Artificial | 190 |
| Water | 210 |
| Other Lands | 200, 201, 202, 220 |
Table 4: Land Cover aggregation method
| Land Cover Class | Value |
| Forest Land/ Tree Covered | 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100 |
| Grassland | 40, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153 |
| Cropland | 10, 11, 12, 20, 30 |
| Wetlands | 160, 170, 180 |
| Settlements / Artificial | 190 |
| Water | 210 |
| Other Lands | 200, 201, 202, 220 |
Table 5: Land Use/ Land Class Change Dynamics
| IPCC Class | Forest Land | Grassland | Cropland | Wetland | Settlement | Other Land |
| Forest Land | Stable | Vegetation Loss | Deforestation | Inundation | Deforestation | Vegetation Loss |
| Grassland | Afforestation | Stable | Agriculture Expansion | Inundation | Urban Expansion | Vegetation Loss |
| Cropland | Afforestation | Withdrawal of Agriculture | Stable | Inundation | Urban Expansion | Vegetation Loss |
| Wetland | Woody Encroachment | Wetland Drainage | Wetland Drainage | Stable | Wetland Drainage | Wetland Drainage |
| Settlement | Afforestation | Vegetation Establishment | Agriculture Expansion | Wetland establishment | Stable | Withdrawal of Settlement |
| Other Land | Afforestation | Vegetation Establishment | Agriculture Expansion | Wetland establishment | Urban Expansion | Stable |
Figure 2: Flowchart for the study
- RESULTS & DISCUSSION
- Land Use Land Cover Spatiotemporal Change Analysis
The purpose of objective one was to produce land use land cover for Lake Chad Basin using recent imagery in other to understand the LULC dynamics within the period under review for the study area. The land use land cover shown in fig 4.1 for 2001 shows that tree cover has 242314.10 amounting to (10.09%) percentage, out of a total land mass 3615682.12sqm, which located in the southern part of the Lake Chad basin revolving towards the west, the grassland occupying a land mass of 530,092.20 of a percentage of 14.7% out of a total mass area 361,5682.12 situated at North Central and Southern part of the Lake Chad basin. Croplands consist of 12.2% (440,905.90 sq.km) of an area mass of 361,5682, cropland cuts across the eastern to the western and the southern parts of the Lake Chad basin. the 0.38% (13,608.30) of the wet land covered an area of a total mass of 361,5682.12, located at the central east of the Lake Chad and hovering around southern parts of the Lake Chad basin.
The built-up area comprises of 0.027% (638.2) of the land mass of 2,402,370 located at the eastern part of the Lake Chad basin, the northern part of the Lake Chad basin is mostly bare land made up of 1,169,434.80 of land mass resulting to 48.7% out of the total land mass of the Lake Chad basin, the water body of 0.22% (5,376.60 sq.km) was found mostly around the lake and some basin close to the chari logone towards the southern part of the Lake Chad basin. Grassland shows that there is a decrease in the populated area which occupies the north central and southern part of the Lake Chad basin with a shape decrease in percentage coverage of 21.64 2001 as against 22.07% (22.06538451) for the period of 2010 which was 18.35% 4(40,905.90) 2001 thus showing there is a gain in momentum of 18.73% (449,852.20) of Cropland 2010 cutting across the eastern and western region of the Lake Chad basin. 0.56% (13,608.30) was recorded in 2001 while in 2010, there was slight increment of 0.58% (13,992.90) in the wetland region which cuts across the central eastern part of the Lake Chad and spanning towards the southern hemisphere. However, indication shows that there was a slight increase in 0.035% 2010 as compared to 0.027% in the built-up areas preceding the year 2001. The bare land here still maintained its statistics of 48.67% (1,169,434.80s) with a marginal decrease in fractional digits of 48.60% (1,167,616.00) in the marked area of the Lake Chad, water bodies are observed to have decreased in 2010 showing a percentage of 0.21% (5,031.80) as recorded against the 2001 of 0.22% (5,376.60) of the total landmass.
Figure 4.1: Reclassified Map of Lake Chad Basin 2001
Figure 4.2: Reclassified Map of Lake Chad Basin 2010
Figure 4.3: Reclassified Map of Lake Chad Basin 2020
The tree cover land of 2020 was noted to have appreciated to a percentage of 10.24% (245,926.70 sq.km) of the Lake Chad basin while grass lands amounted to 21.95% (527,317.80 sq.km), Wetlands cover a land mass of 0.58% (13,893.60 sq.km) built up areas also consist of 0.06% (1,479.50 sq.km) and water bodies have a total of 0.21% (5,083.10 sq.km) respectively as shown in fig 4.3. The summary of the percentage cover from 2001 to 2020 is reported in table 4.1.
Table 4.1: Annual Land Use Land Cover Classification for Lake Chad
| Tree-covered areas | Grassland | Cropland | Wetland | Artificial surfaces | Other land | Water bodies sq. km | Vegetal Land | |
| 2001 | 242,314.1 | 530,092.2 | 440,905.9 | 13,608.3 | 638.2 | 1,169,434.8 | 5,376.6 | 1,213,312.11 |
| 2002 | 242,226.6 | 529,738.0 | 441,286.4 | 13,671.9 | 663.1 | 1,169,456.0 | 5,327.8 | 1,213,251.08 |
| 2003 | 242,977.3 | 527,750.6 | 442,400.0 | 13,866.9 | 680.3 | 1,169,579.9 | 5,115.0 | 1,213,127.86 |
| 2004 | 243,332.8 | 524,886.5 | 445,045.8 | 13,877.0 | 695.1 | 1,169,421.9 | 5,110.9 | 1,213,265.06 |
| 2005 | 243,668.2 | 524,096.3 | 445,557.6 | 13,894.9 | 726.1 | 1,169,310.8 | 5,116.1 | 1,213,322.11 |
| 2006 | 243,966.2 | 523,090.3 | 445,972.3 | 13,957.4 | 748.5 | 1,169,591.5 | 5,043.9 | 1,213,028.82 |
| 2007 | 244,258.1 | 522,370.3 | 446,954.1 | 13,966.2 | 770.6 | 1,169,013.9 | 5,036.8 | 1,213,582.43 |
| 2008 | 244,924.8 | 520,875.2 | 448,046.0 | 13,971.9 | 798.2 | 1,168,721.4 | 5,032.3 | 1,213,846.13 |
| 2009 | 244,963.5 | 519,200.2 | 449,873.0 | 13,976.0 | 818.4 | 1,168,503.7 | 5,035.2 | 1,214,036.71 |
| 2010 | 244,968.1 | 520,069.8 | 449,852.2 | 13,992.9 | 839.2 | 1,167,616.0 | 5,031.8 | 1,214,890.10 |
| 2011 | 244,862.6 | 520,883.1 | 450,296.2 | 14,032.5 | 857.5 | 1,166,426.6 | 5,011.4 | 1,216,042.00 |
| 2012 | 244,801.3 | 521,950.9 | 450,529.6 | 14,036.5 | 889.7 | 1,165,153.4 | 5,008.5 | 1,217,281.78 |
| 2013 | 244,767.8 | 521,876.1 | 450,798.8 | 14,027.6 | 952.5 | 1,164,937.1 | 5,010.0 | 1,217,442.77 |
| 2014 | 244,839.9 | 521,698.6 | 451,817.7 | 14,006.4 | 1,005.8 | 1,163,964.6 | 5,036.9 | 1,218,356.31 |
| 2015 | 244,839.8 | 521,694.6 | 451,787.5 | 14,006.4 | 1,040.3 | 1,163,964.6 | 5,036.8 | 1,218,321.86 |
| 2016 | 244,910.4 | 522,173.1 | 451,965.0 | 13,987.6 | 1,187.7 | 1,163,089.6 | 5,056.6 | 1,219,048.50 |
| 2017 | 245,020.1 | 522,405.1 | 452,033.8 | 13,997.6 | 1,236.6 | 1,162,616.6 | 5,060.2 | 1,219,459.00 |
| 2018 | 245,357.9 | 526,793.2 | 452,428.7 | 13,900.8 | 1,260.2 | 1,157,562.0 | 5,067.2 | 1,224,579.77 |
| 2019 | 245,952.4 | 527,251.6 | 453,776.2 | 13,896.6 | 1,410.7 | 1,155,003.3 | 5,079.1 | 1,226,980.22 |
| 2020 | 245,926.7 | 527,317.8 | 453,871.8 | 13,893.6 | 1,479.5 | 1,154,797.5 | 5,083.1 | 1,227,116.33 |
- Change Dynamics of the Surface Area of Lake Chad Basin
The purpose of objective two was to analyze the change dynamics of the surface area of Lake Chad Basin within the period under review for the study area. Results of change dynamics in the study area from 2001 to 2020 as reported in table 4.2, shows
- Maximum change recorded was from bare-land to grasslands at 19,116.88km2 followed by bare-land to grassland at 16,098.24km2.
- The least change recorded was within the built-up class transiting to tree-covered, grassland, croplands, wetlands, and bare-land at below 0km.
- Highest quantity of waterbody loss was to the wetlands.
Grassland indicated to have changed to Tree cover with a Landmass of 5,696.07 sq. km (%) while Grassland to grassland was observed to have no changes but stable with 503,584.97sqkm, there was an agricultural expansion during this period thus 19,116.88sq km of grass land was converted to crop land. Grassland lost to wetlands with a landmass of 55.41sqkm and lost to bare land due to urban expansion.
Table 4.2: Change dynamics of the surface area of Lake Chad Basin (sq. km) from 2001 to 2020.
| AREA OF LAND USE IN 2020 | ||||||||
| AREA OF LAND USE IN 2001 | Tree-covered areas | Grasslands | Croplands | Wetlands | Artificial areas | Other lands | Water bodies | |
| Tree-covered areas | 238,832.47 | 2,845.24 | 488.22 | 133.40 | 4.50 | 0.24 | 8.26 | |
| Grasslands | 5,696.07 | 503,584.97 | 19,116.88 | 55.41 | 140.54 | 1,485.18 | 14.57 | |
| Croplands | 1,106.87 | 4,776.29 | 434,189.34 | 76.89 | 694.90 | 0.91 | 57.27 | |
| Wetlands | 284.50 | 2.92 | 2.48 | 13,264.12 | 0.00 | 0.36 | 53.86 | |
| Artificial areas | 0.00 | 0.00 | 0.00 | 0.00 | 638.19 | 0.00 | 0.00 | |
| Other lands | 0.24 | 16,098.24 | 14.41 | 1.70 | 1.15 | 1,153,306.13 | 9.86 | |
| Water bodies | 4.84 | 11.57 | 57.09 | 362.03 | 0.18 | 1.58 | 4,939.30 | |
Furthermore, the land use change of 2001 to 2020 showed a total of 1,106.87sq area of Crop land being lost to Tree cover while Cropland receded to Grassland with a land mass of 19,116.88sq. km due to lack of farming activities. It also showed stability in the use of cropland, the area of 434,189 .34sq was seen to be the same from 2001 to 2020. Figures 4.4 to 4.6 shows a pie chat representation of land use and land cover in the study area from 2001 to 2020.
Figure 4.4: Pie-Chat Representation of land use, land cover Lake Chad Basin 2001
Figure 4.5: Pie-Chat Representation of land use, land cover Lake Chad Basin 2010
Figure 4.6: Pie-Chat Representation of land use, land cover Lake Chad Basin 2020
Crop land lost to Wetland with a landmass of 76.89 sq.km, crop land to built-up area with 694.90sq., and Crop land to bare-land was at 57.27 sq. km, this is as a result of Urban expansion and vegetation loss in the region.
There is a barely wooden encroachment of Tree cover on wetland with a total land mass of 238,832.47 sq. However, the wetland to grasslands indicates the presence of a drainage and there was also an emergence of croplands to Wetland giving rise to a dynamic change of wet drainage. The changes between built up to wetland and wetland to wetland were not recorded consequently there was stability in the area in the period of 2001 to 2020.
Table 4.3: Land Use/ Land Class Change Dynamics
| IPCC Class | Forest Land | Grassland | Cropland | Wetland | Settlement | Other Land |
| Forest Land | Stable | Vegetation Loss | Deforestation | Inundation | Deforestation | Vegetation Loss |
| Grassland | Afforestation | Stable | Agriculture Expansion | Inundation | Urban Expansion | Vegetation Loss |
| Cropland | Afforestation | Withdrawal of Agriculture | Stable | Inundation | Urban Expansion | Vegetation Loss |
| Wetland | Woody Encroachment | Wetland Drainage | Wetland Drainage | Stable | Wetland Drainage | Wetland Drainage |
| Settlement | Afforestation | Vegetation Establishment | Agriculture Expansion | Wetland establishment | Stable | Withdrawal of Settlement |
| Other Land | Afforestation | Vegetation Establishment | Agriculture Expansion | Wetland establishment | Urban Expansion | Stable |
In table 4.3, showing LULC change dynamics and corelation reveals that from 2001 to 2020 forestland showed a stable correlation, during this period, forestland changed to grassland with 2,845.24sq km, 488.22sq km of forestland was also converted to cropland, tree cover was seen to have slightly lost a landmass of 133.40sq to wetland while Built-up gained 4.50sq km of tree cover with a vegetation loss to bare land. There was a significant appreciation of tree cover land use. 0.00sqkm of built up in the table above indicates the activity of afforestation while that of cropland was due to vegetative growth in the area. it further shows changes for agricultural expansion in the region between the built up and crop lands. The period of 2001 to 2020 recorded land use of bare land being converted to wetland while bare land to built-up area showed no outstanding changes thus bare land dynamic change as recorded above revealed a discontinuation of settlement in the area. Bare land to tree cover showed a remarkable change of land conversion due to afforestation, comparing it to grasslands, the activity of vegetation is significantly recorded in the region in the year 2020. Croplands indicated an agricultural expansion as against 2001, While bare lands were converted to wetland, the built-up area experienced an urban expansion and bare land to bare land remained stable.
- Impact of Climate Variables on Lake Chad Surface Water
The purpose of objective three was to determine the impact of climate variables on Lake Chad surface water within the period under review for the study area. The climate variables reviewed include; rainfall, evapotranspiration, soil moisture and surface runoff. An indebt analysis of precipitation is reported below. The months of May to October experienced higher amount of rainfall in the time series, this indicates a wet season as seen in table 4.4. The highest amount of rainfall occurred in the month of August (2583.48mm) followed by July (2159.19mm) as seen in figure 4.7. The rainfall reduces between the months of November and April. This indicates the dry seasons with the driest months being December (28.54mm) and January (26.77mm).
Table 4.4: Cumulative Monthly Rainfall trend for Lake Chad Basin from 2001 to 2020
| Month | Monthly Rainfall(mm) |
| Jan | 26.77 |
| Feb | 69.82 |
| Mar | 226.64 |
| Apr | 524.46 |
| May | 1043.35 |
| Jun | 1384.87 |
| Jul | 2159.19 |
| Aug | 2583.48 |
| Sep | 1735.19 |
| Oct | 1008.42 |
| Nov | 164.8 |
| Dec | 28.54 |
Figure 4.7: Cumulative Monthly Rainfall trend for Lake Chad Basin from 2001 to 2020
An assessment on annual basis was done the reported in table 2. The driest year in the time series was (2001) with a minimum rainfall of 467.92 mm. There was an increase in the annual rainfall in the years 2003 (540.30mm), 2010(561.48mm), 2012 (559.06mm) while the highest amount of rainfall 566.1mm occurred in 2019. The value for Zmk = 0.2 shows there is a positive trend in the time series as reported in figure 4.8. The positive trend is statistically not significant because, the computed p-value is greater than the significance level 0.05.
Figure 4.8: Time series of annual rainfall in Lake Chad Basin 2001-2020
Table 4.5: Annual and Seasonal Rainfall from 2001- 2020
| YEARS | ANNUAL RFL (mm) | SEASONS (mm) | |||
| Dry | Late Dry | Wet | Late Wet | ||
| 2001 | 467.92 | 2.8 | 80.11 | 273.32 | 111.82 |
| 2002 | 508 | 3.48 | 78.16 | 277.08 | 149.2 |
| 2003 | 540.31 | 6.37 | 80.95 | 307.49 | 145.79 |
| 2004 | 491.34 | 3.25 | 77.79 | 271.05 | 139.06 |
| 2005 | 521.06 | 7.18 | 80.1 | 307.87 | 124.58 |
| 2006 | 518.14 | 10.22 | 87.89 | 272.49 | 149.53 |
| 2007 | 512.14 | 3.15 | 89.37 | 279.16 | 139.85 |
| 2008 | 534.2 | 2.46 | 103.28 | 307.78 | 117.7 |
| 2009 | 527.55 | 9.96 | 87.39 | 288.57 | 144.9 |
| 2010 | 561.48 | 5.56 | 78.26 | 310.26 | 167.55 |
| 2011 | 525 | 8.36 | 75.83 | 298.3 | 142.51 |
| 2012 | 559.06 | 7.29 | 90.82 | 318.67 | 141.91 |
| 2013 | 526.74 | 8.55 | 92.79 | 287.49 | 135.56 |
| 2014 | 511.6 | 9.07 | 103.77 | 268.64 | 132.27 |
| 2015 | 510.44 | 7.74 | 73.7 | 287.34 | 142.15 |
| 2016 | 528.08 | 2.14 | 93.91 | 296.44 | 134.15 |
| 2017 | 524.21 | 5.04 | 87.38 | 306.59 | 124.94 |
| 2018 | 532.57 | 10.9 | 94.38 | 290.44 | 137.79 |
| 2019 | 566.1 | 8.67 | 94.58 | 297.64 | 165.91 |
| 2020 | 506.46 | 1.27 | 82.12 | 292.94 | 130.36 |
| TOTAL | 123.46 | 1732.58 | 5839.56 | 2777.53 | |
Further assessment was done on a seasonal scale. Results showed that the amount of rainfall for each season is quite slightly different as shown in table 4.5. The highest amount of rainfall 5839.56mm occurred in the wet season subsequently by the late wet with 2777.53mm rainfall. The rainfall during the driest season is 123.46mm. Figure 4.9, 4.10, 4.11 show positive and upward trend for three seasons respectively Dry (Z =,0.15), Late Dry (Z= 0.29), Wet (Z=0.16) and fig 4.12 shows the Late Wet season with a negative and downward trend (Z= -0.10), Table 4.6, summarizes the values for the Mann Kendal trend analysis showing the trend ,trend magnitude (m) and significance level. The computed p-value is greater than the significance level 0.05 for the four seasons. Figure 4.13 and 4.14 show the rainfall map for the study area.
Table 4.6: Seasonal and Annual Trend analysis of Lake Chad Basin (Mann Kendall and Sen’s slope
| Time Series | Min Rainfall
(mm) |
Max Rainfall
(mm) |
Mean | Std Dev | Sen’s Slope | Signific. (p) | Mann Kendall
(Z) |
Rainfall Trend |
| Dry | 1.27 | 10.9 | 6.173 | 3.03 | 0.14 | 0.38 | 0.15 | positive |
| Late Dry | 73.70 | 103.77 | 86.63 | 8.75 | 0.59 | 0.07 | 0.29 | positive |
| Wet | 268.64 | 318.67 | 291.97 | 14.95 | 0.93 | 0.3 | 0.16 | positive |
| LateWet | 111.82 | 167.55 | 138.87 | 13.81 | -0.41 | 0.58 | -0.10 | negative |
| Annual | 467.92 | 566.1 | 523.62 | 23.15 | 1.12 | 0.2 | 0.20 | positive |
Figure 4.9: Trend of dry season rainfall
Figure 4.10: Trend of late dry season rainfall
Figure 4.11: Trend of wet season rainfall
Figure 4.12: Trend of late wet season rainfall
Figure 4.13: Rainfall Map of Lake Chad Basin for 2010
Figure 4.14: Rainfall Map of Lake Chad Basin for 2020
Figure 4.15: Evapotranspiration Map of Lake Chad Basin for 2001
Figure 4.16: Evapotranspiration Map of Lake Chad Basin for 2010
Figure 4.17: Evapotranspiration Map of Lake Chad Basin for 2020
Table 4.7: Seasonal and Annual Trend of Evapotranspiration
| Year | Annual Evapotranspiration (mm) |
| 2001 | 328.24 |
| 2002 | 315.16 |
| 2003 | 346.40 |
| 2004 | 317.38 |
| 2005 | 334.30 |
| 2006 | 331.66 |
| 2007 | 320.26 |
| 2008 | 303.10 |
| 2009 | 304.51 |
| 2010 | 322.66 |
| 2011 | 324.90 |
| 2012 | 350.15 |
| 2013 | 329.83 |
| 2014 | 316.66 |
| 2015 | 317.26 |
| 2016 | 332.05 |
| 2017 | 323.44 |
| 2018 | 335.30 |
| 2019 | 355.80 |
| 2020 | 347.45 |
Figure 4.18: Mean Annual Total Evapotranspiration for LCB (mm)
Figure 4.15 to figure 4.17 shows the map of evapotranspiration for series under review with details contained in table 4.7. Figure 4.18 shows the mean annual total evapotranspiration for Lake Chad basin of 2001 was 328.24mm while there was an intense decrease in 2010 by 322.66mm and the volume increased significantly to 347.45mm in 2020. Table 4.8 and 4.9 show the surface runoff and soil moisture for the 20 years. 2012 had the highest surface run off of 3.20 and the lowest was in 2009 with a value of 1.12. Table 4.9 shows the surface soil moisture was highest in 2019 with a value of 20.25 and the lowest value was 18.70 in 2002. Fig. 4.19 shows a plot surface soil moisture.
| Year | Annual Runoff (mm) |
| 2001 | 2.43 |
| 2002 | 1.61 |
| 2003 | 2.05 |
| 2004 | 1.59 |
| 2005 | 1.77 |
| 2006 | 1.80 |
| 2007 | 1.65 |
| 2008 | 1.58 |
| 2009 | 1.12 |
| 2010 | 2.47 |
| 2011 | 2.11 |
| 2012 | 3.20 |
| 2013 | 2.05 |
| 2014 | 1.69 |
| 2015 | 1.97 |
| 2016 | 1.72 |
| 2017 | 2.51 |
| 2018 | 2.07 |
| 2019 | 3.04 |
| 2020 | 2.66 |
Table 4.8 Surface Runoff Table 4.9: Surface Soil Moisture
| Year | Surface Soil Moisture (mm) |
| 2001 | 18.90 |
| 2002 | 18.71 |
| 2003 | 19.28 |
| 2004 | 18.70 |
| 2005 | 19.09 |
| 2006 | 19.09 |
| 2007 | 18.98 |
| 2008 | 18.98 |
| 2009 | 19.03 |
| 2010 | 19.79 |
| 2011 | 19.19 |
| 2012 | 20.13 |
| 2013 | 19.39 |
| 2014 | 19.30 |
| 2015 | 19.27 |
| 2016 | 19.59 |
| 2017 | 19.51 |
| 2018 | 19.82 |
| 2019 | 20.25 |
| 2020 | 20.02 |
Figure 4.19: Surface Soil Moisture (10cm)
- LCB Surface Water Against Climate Variables
Fig 4.20 to 4.23 shows a regression plot the lake chad basin waterbody against climate variables with the water body being the dependent variable and the different climate variables as the independent variables. Fig 4.20 shows the relationship between water body and soil moisture. The coefficient of determination R2 value is 0.165 and therefore implies a strong or large association.
Fig 4.21 shows the relationship between water body and evapotranspiration. The coefficient of determination R2 value reads 0.0003 implying a weak relationship or association. Fig 4.22 shows the relationship between water body and rainfall. The coefficient of determination R2 value reads 0.3318 implying a strong relationship or association. Fig 4.23 shows the relationship between surface runoff and surface soil moisture. The coefficient of determination R2 value reads 0.6 implying a strong relationship or association.
Figure 4.20: Water Body against Soil Moisture
Figure 4.21: Water body against Evapotranspiration
Figure 4.22: Water Body against Rainfall
Figure 4.23: Plot of Annual Surface Runoff against Surface Soil Moisture for LCB.
- Determination of the Response of Lake Chad Water Change to Vegetation/NDVI
The purpose of objective four was to analyze the change dynamics of the surface area of Lake Chad Basin within the period under review for the study area. There was a steadily increase in the NDVI between the years 2001 to 2014, then a sharp drop to 0.253 in 2015. After this, a strident increase was noted till it attained the highest value of 0.267 in 2020. Over this period, the lowest value is 0.249 in 2001 and the highest was 0.267 in 2020
Figure 4.24: Annual NDVI for LCB
- Lake Chad Surface Water Against Vegetation and NDVI
Figure 4.25: Water Body against Vegetal Land
Fig 4.25 to 4.27 shows a regression plot the lake chad basin waterbody against vegetation and NDVI with the water body being the dependent variable and the different climate variables as the independent variables. Fig 4.25 shows the relationship between water body and vegetal land. The coefficient of determination R2 value is 0.0558 and therefore implies a weak association. Fig 4.26 shows the relationship between water body and NDVI. The coefficient of determination R2 value reads 0.1494 implying a strong relationship or association. Fig 4.27 shows the relationship between water body and rainfall. The coefficient of determination R2 value reads 0.3318 implying a strong relationship or association. Fig 4.27 shows the relationship between vegetal land and rainfall. The coefficient of determination R2 value reads 0.08 implying a moderate relationship or association.
Figure 4.26: Water body against Annual NDVI
Fig 4.27 Vegetal Land Against Rainfall
Fig 4.28 Water body against vegetal land
Fig 4.29: Evapotranspiration Against Vegetal Land
Fig 4.28 shows the relationship between water body and vegetal land. The coefficient of determination R2 value is 0.055 and therefore implies a weak association. Fig 4.29 shows the relationship between evapotranspiration and vegetal land. The coefficient of determination R2 value reads 0.02 implying a weak relationship or association.
Figure 4.30: Time series graph for the unsmoothed and temporally smoothed NDVI data.
Unsmoothed NDVI data in blue and temporally smoothed NDVI data in red. Very noticeable reductions in the values of unsmoothed NDVI signifies the presence of clouds as well as other types of atmospheric related contamination. The smoothing algorithm is being used to effectively correct the erroneous NDVI values based on the features of the valid NDVI curve.
Figures 4.31, 4.32 and 4.33 shows the NDVI for the Lake Chad basin in intervals of 10 years. Generally, below average shows that the healthiness of vegetation is below average and the value ranges from 0-94; this is evident in the northern part of the area, while average shows an average level of healthiness of vegetation and the value ranges from 95-105. Above average depicts the healthiest of vegetation in the area studied and the values are greater than 105; this is seen in the southern part of the area. For figure 4.31(year 2002), the zone with average healthiness of vegetation is clearly distinct from the below average and above average zones. In figure 4.32(year 2010), there is a slight gradation of the healthiness (above average region) into the average zone while in Figure 4.32, major changes were noticed with the healthiness slightly skewed to the margins of the Lake Chad with some major regrowth also around the south-western portion of the Lake Chad Basin.
Figure 4.31: NDVI of the Lake Chad Basin for 2002
Figure 4.32: NDVI of the Lake Chad Basin for 2010
Figure 4.33: NDVI of the Lake Chad Basin for 2020
List of MDA’s to Implement the Project Recommendations
- Federal Ministry of Agriculture and Rural Development
- Federal Ministry of Water Resources
- Lake Chad Basin Commission
- Federal Ministry of Environment
- Food and Agriculture Organizations
- NiMet (Nigerian Meteorological Agency)
- Nigeria Integrated Water Resources Management Commission (NIWRMC)
- Budget and Planning of the Federation
- Sustainable Development Goals Office
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