Influence of Land Use Land Cover Change on Groundwater Recharge in the Continental Terminal Area of Abidjan, Ivory Coast

The process by which rainfall reaches the aquifer in a sedimentary area is infiltration. This process could be affected quantitatively or qualitatively by the changes in the land use land cover (LULC) as a result of anthropogenic activities which could affect groundwater reserves. This study focuses on the influence of LULC change on groundwater recharge in the context of urbanization and population growth. Four weather stations data and satellite image data were used in order to evaluate water infiltration which is the amount of water that reaches the piezometric surface from 1990 to 2016. The spa-tial-temporal LULC change in relation to urbanization 2000 and 26%, 14% in 2016 of rainfall and show their strong dependence on precipitation and LULC change.


Abstract
The process by which rainfall reaches the aquifer in a sedimentary area is infiltration. This process could be affected quantitatively or qualitatively by the changes in the land use land cover (LULC) as a result of anthropogenic activities which could affect groundwater reserves. This study focuses on the influence of LULC change on groundwater recharge in the context of urbanization and population growth. Four weather stations data and satellite image data were used in order to evaluate water infiltration which is the amount of water that reaches the piezometric surface from 1990 to 2016. The spatial-temporal LULC change in relation to urbanization sprawl was assessed based on a series of Landsat images for 1990, 2000 and 2016. The maximum likelihood pixel-based on classification method was used to analyze the spatial-temporal LULC dynamics. The Thiessen polygon method was used for the mean area precipitation computation. The recharge was determined using water balance method after determining the runoff based on the Soil Conservation Service curve number method. The results show an increase in built-up and agricultural land, while the forest and shrub areas declined with water body remaining unchanged over the period 1990-2016. The decline in forest could be imputed to the demographic and socio-economic growth as expressed by the expansion of agriculture and urbanization. Groundwater recharge and runoff results are respectively 34%, 20% in 1999; 21%, 46% in

Introduction
Nowadays, over 50% of the Earth's population now lives in cities and it is estimated that by 2025 this leads to an increase in population over 67% [1]. The anthropogenic activities on the environment increase impervious cover and storm drains that channel precipitation off roads [2]. Impervious cover is a major index of urbanized areas and is considered the most pervasive, relevant characteristic leading to hydrologic impacts [3]. Furthermore, the rapid growth of urban areas has two basic effects on groundwater resources, such as effects on natural recharge of aquifers due to sealing of ground with concrete, and pollution of groundwater due to leakage from drainage and, industrial wastes and effluents [4]. Nevertheless the contribution of groundwater, the part of groundwater in water consumption is extremely important for water supply for most of the urban area [5]; therefore it will be important to analyze urbanization influence on groundwater. In this study, we will see the case study of Abidjan groundwater. Continental Terminal aquifer in the South East of Ivory Coast, commonly known as Abidjan aquifer, is the only source of drinking water supply in the Abidjan district since the independence of Ivory Coast. This unconfined groundwater plays an important role providing water for the population of Abidjan city. They need to supply water for private, public, agriculture, industrial and commercial uses. The population has increased considerably in recent decades due to an exodus of population from north to south. This growth has been accompanied by strong urbanization and uncontrolled industrial growth [6]. The variability of precipitation and the urbanization process affect more and more the quality and the quantity of this aquifer [7] [8]. For instance, the decrease of piezometric level results from the increase of soil sealing. In fact, water infiltration process depends on the grounds slope, the unsaturated zone and the LULC [9]. Therefore the increase in bare soils and urbanized areas contributed to a decrease in infiltration and take advantage of runoff [10].
Relevant studies were carried out by several researchers [11]- [17] in the past.
These researches highlight several methods for determining the recharge, such as water table fluctuation method and the water balance method. Water Table   Fluctuation is the most used, especially in the context of shallow unconfined aquifers. It is simple, easy to use. The main constraint of the method is based on the difficulty of evaluating the specific storage coefficient (Sy), which directly K. K. Abdelaziz et al. Journal of Water Resource and Protection controls the estimated value of the recharge. This method also considers that any rise in the water level of the water table is due to recharge. However, water pumping or evapotranspiration could question this hypothesis. For the water balance method, it requires some parameters such as precipitation, runoff and actual evaporation but runoff determination method remains the most important to find a good result of water recharge. Rainfall and runoff are significant sources of water for recharge, therefore, evaluation of water availability by an understanding of rainfall and runoff is essential [18]. The Soil Conservation Service Curve Number method (SCS-CN Method) used in this study to determine runoff comes to overcome the limitations of previous methods. The SCS-CN (1985) method has been established in 1954 by the USDA SCS [19], defined in the Soil Conservation Service (SCS) by National Engineering Handbook (NEH-4) Section of Hydrology [20]. The Soil conversations Service-Curve Number approach is based on the water balance calculation and two fundamental hypotheses had been proposed [21]. This method was used by [22] [23] to estimate runoff potential from rainfall and by [24] in order to assess floodwater. The objective of this study is to estimate surface runoff and groundwater recharge of Abidjan aquifer for the year 1990, 2000 and 2016 using the water balance method taking into account the hydrologic soil group of the area, the different LULC, treatment and hydrologic conditions.

Study Area
The Continental Terminal (Figure 1 North, then savannah of palmyra in the South. There are also several plant landscapes such as dense moisture forest; pre-lagoon savannas; mangroves and swamp forests. According to [28], the soils of the study area ( Figure 2

Climate Data
There are four meteorological stations situated in the study area ( Figure 3), with daily rainfall from 1983 to 2017 and air temperature from 1960 to 2017. Table 1 shows the characteristics of the climate stations used in the study. The climate

Land Use/Land Cover Data
Available After the completion of the ordered data, it was processed and classified using the maximum likelihood pixel-based supervised classification method.

Mean Areal Precipitation Estimation at a Spatial Scale
Thiessen polygon method is used for computing the mean area precipitation for a catchment from rain gauge observations. One of the assumptions of this method is that linear rainfall distribution exists; hence, many researchers recommend that the use of the method should be restricted to relatively flat areas with linear rainfall distribution. Arguments remain, however, concerning the optimal gauge density and spacing conditions for its application. Several authors recommend the method's use for areas characterized by a relatively dense and uniformly spaced rain-gauge network. [29] for example, considers that the method is satisfactory with even rainfall distribution, a good 3.2. Unigauge network, and flat country. [30] also says that the method is most applicable in densely gauged networks.

Land Use/Land Cover Data Analysis Method
Mapping LULC accurately and efficiently using remote sensing images requires a good image classification method. Unfortunately, there are many factors which could affect the effectiveness and accuracy of the classification. The maximum likelihood pixel-based on classification method, which was developed in the 1970s, is the most commonly method used on Landsat images processing [31] [32]. This method was used to produce the LULC maps for the years 1990, 2000 and 2016 by considering five (5) LULC types namely: urban area, shrubs, agricultural, forest and water ( Table 2).
The maximum likelihood method calculates according to the reflectance of the pixels, the probability of their belonging to a given class. The pixel is assigned to  was performed to build a confusion matrix [33]. In order to validate the land cover classification, an accuracy assessment in the form of an error matrix was performed as recommended by [34], and the kappa coefficient was used as the statistical parameter. The kappa coefficient (K) determination is given by Equation (1), is multivariate used in accuracy assessment of thematic maps. It is an efficient method to derive information from an image via the confusion matrix.
K > 0.80 represents strong agreement and good accuracy; K between 0.40 and 0.80 indicates a moderate accuracy, and K < 0.40 represents a poor accuracy [35] The overall accuracy and producer's accuracy based on the produced confusion matrix were determined using Equation (2).
Spectral signature analysis of satellite images has identified five main classes of land use: The kappa coefficient is given by the Equation (1).
where r is the number of rows in the matrix, ij x is the number of observations in row i and column i, i x + and i x + are the marginal totals of row i and column i, respectively, and N is the total number of observations.

Number of orrected pixel
Overall accuracy 100 Total number of elected pixel = × (2)

Groundwater Recharge Estimation Using Water Balance Method
Groundwater recharge is not easy to estimate mainly in the urban area. In fact, it depends on several factors such as climatic forcing, nature of soil and land use [36]. Furthermore, there are many methods to estimate Groundwater recharge. AET P R I S = + + + ∆ with P: total rainfall (mm), AET: actual evapotranspiration (mm), R: runoff (mm), I: infiltration, ∆S: water stock variation in the available water content (AWC).

Actual Evapotranspiration (AET)
The AET is the amount of water which is actually evaporated and depends on many parameters such as: precipitation, temperature, insolation, wind, vegetation, nature of the soil, useful soil reserve [39]. To estimate this parameter, there are many methods; however in this study Thornthwaite method was used. It is a widely used empirical method for estimating evapotranspiration and the only variable used is monthly temperature.

Surface Runoff Determination
The surface runoff (R) was predicted using a hydrological model which utilizes the USDA (United States Department of Agriculture) procedure for the estimation of surface runoff which is based on the SCS-CN (Soil Conservation Service Curve Number) method. The flowchart in Figure 4 summarizes the methodology adopted for the runoff estimation. After using the satellite image to determine the LULC cover of the area, the identified soil types at the study were distributed into hydrological soil groups (A, B, C and D) according to the infiltration capacity of soil (Table 3). The area of each polygon with a unique assigned curve number was determined from the superimposed LULC and hydrological soil group based on standard SCS curve number (Table 4). The SCS-CN approach is a frequently used empirical method based on the water balance approach to estimate the direct runoff from a watershed [40]. The curve number for each drainage basin of area-weighting was calculated from the land use-soil group polygons within the drainage basin boundaries [41]. Daily rainfall data was chosen because the Antecedent moisture condition classes (Table 3)     Subsequently, initial abstraction (surface storage, interception, and infiltration) was computed using the following equation: For all values with precipitation greater than the initial abstraction (I a ), the daily runoff depth for all the specific days of the year was computed using the SCS-CN Equation (6)  I a = Initial abstraction (surface storage, interception, and infiltration, mm) S = potential maximum retention (mm) CN has a range from 30 to 100; lower numbers indicate low runoff potential, while larger numbers are for higher runoff potential. The lower the curve number the more permeable the soil.

Antecedent Moisture Condition Classes
Soil water content on the day of the storm is accounted for by an antecedent moisture condition (AMC) determined by the total rainfall in the 5-day period preceding the storm. Three AMC groups have been established with the boundaries between groups dependent upon the time of year as shown in Table 4. As time of year there is growing season which is the part of the year during which rainfall and temperature allow plants to grow and dormant season is the time when a plant has naturally stopped growing.

Classification Accuracy
The results for the Landsat images classification accuracy for the three LULC maps evaluated using confusion matrix are shown in Table 5. An overall accuracy and a kappa index of 98.29% and 0.97 for the year 1990, 99.90% and 0.99 for 2000 and 93.80% and 0.92 for 2016 were obtained. The confusion matrix highlights the map accuracies ranging from 94% to 100% in 1990. In 2000 these accuracies are from 99% to 100% and from 78% to 98% in 2016. The results reveal satisfactory values of kappa coefficient (>0.90) and cartographic accuracy of more than 90%. According to [43], a Kappa index of more than 0.50 indicates that the classified LULC map can be used for further analysis. These results are close to those of several studies such as [   The LULC statistic results in Figure 5 shows that water bodies occupy most parts of the southern and the eastern part of the catchment with almost the same proportion from 1990 to 2016. The forest was found to be decreasing in size over time from 1990 to 2016. In fact, forest land was being cleared and converted into a shrubland, cropland or urban area. However, the urban area which affects the permeability of the soil is present in the central part of the study area and in the south eastern part in 1990, and spreads across the entire study area in the year 2000. The urban area was observed to be increasing at a higher rate from the year 1990 to 2016. Furthermore, shrubland which also affects soil permeability was scattered throughout the basin in 1990, same to the forest area and decreased in 2000 and 2016 due to the rapid increase of build-up. The agriculture land was found to occupy the smallest area within the watershed and to increase steadily over time ( Figure 6).

Development of Hydrological Soil Group Map
The soil map was used to develop the hydrological soil group map which is a

Curve Number Change and Impacts on Runoff and Recharge in the Study Area
Twelve curve numbers were derived from the three HSG and five LULC classes as shown in the Curve Number maps in Figure 8 and Table 6    increase in the runoff and the decline of recharge in the study area. However, recharge areas identifying areas with low runoff capacity, therefore having a low weighted curve number reappear in 2016.

Seasonal and Inter-Annual Variability of Rainfall, Recharge and Runoff
The results (Table 7)  Inter-annual variability shows that in 2000 when rainfall and recharge were decreasing, runoff was increasing. This period is marked by a strong increase in urbanization in the study area. Figure 9(A) and Figure 9

Discussion
In this study, a spatial-temporal as population growth, and is supported by [46] [49], who found that dry agricultural land could increase groundwater recharge in the southwestern US.
The results show a temporal variability of the recharge on Abidjan aquifer.
The methodology uses to find the recharge highlight runoff variability which brings out the impact of urbanization, shrubs area and agricultural land on groundwater recharge. Thus, the changes in recharge are due to rainfall variability and LULC change over time. Moreover, it is observed that the recharge decreases considerably, almost to a half from 1990 to 2000. This period is marked by a strong increase of urbanization in the study area which favors the increase of runoff from 128 to 181 mm. However, the study reveals an increase in re-Journal of Water Resource and Protection charge in 2016 which is due to the increase of afforested area and agricultural land. In fact, it was observed that covered area favored high infiltration and represents a low weight of curve number which expresses low runoff. However, low infiltration occurs in urban areas where weighted curve number is high and expresses high runoff. The results are in accordance with the study of [10] and some previous studies in West Africa [50] and [51] provide a numbered reference for Baier et al., who affirm that the rapid growth of urban area has two basic effects on groundwater resources such as effects on natural recharge of aquifers due to sealing of ground with concrete and pollution of groundwater due to leakage from drainage and industrial waste and effluents. Furthermore the annual recharge was estimated at 400 mm/year in 2016, this value represents about the double of the results of the study undertaken by [46] this could be explained by the difference in methods used but this result is close to the previous study of [16] who assess the recharge at 342 mm/year in 2006.

Conclusion and Recommendation
This study attempts to bring out influence of LULC change on groundwater recharge for Continental Terminal from 1990 to 2016 using climate stations data and satellite image data utilizing several methods. The study revealed that LULC dynamic is dominated by urbanization area which was increasing from 16% (1990) to 39% (2016). Such a phenomenon had an impact on groundwater recharge in this period. The weight curve number value estimated for the study area is increasing during the study period and that caused an increase in runoff estimates. However, in 2016 it was observing the increase of groundwater recharge which is due to vegetation restoration linked to cultural areas extension.
Furthermore, the study also revealed that rainfall is the most important parameter which influences the recharge during wet season and interannually, it is influenced by both driver, rainfall and LULC change. The outcome of this study gives an insight into the issue of water supply in Abidjan city. In fact this city depends on water supply of the groundwater; therefore to have an idea of the recharge variability could help to predict future water management in the context of climate change and population growth which is not followed by adequate infrastructure. This serves as a guide in developing policies to mitigate the lack of water observed in recent decades.