Using the empirical formulas for estimation of groundwater recharge in the Lobo catchment (Center-West of the Ivory Coast)

 Corresponding author: Yiwa Monique Kamenan Epse Mangoua Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Using the empirical formulas for estimation of groundwater recharge in the Lobo catchment (Center-West of the Ivory Coast) Yiwa Monique Kamenan Epse Mangoua 1, *, Kouamé Jean Olivier Kouadio 2, Gningnéri Souleymane Ouattara 1, Oi Mangoua Jules Mangoua 1 and Brou Dibi 1 1 Laboratory of Science and Technology of Environment, Faculty of Environment, Jean Lorougnon Guédé University, Daloa, Côte d’Ivoire, BP 150 Daloa, Côte d’Ivoire. 2 Laboratory of Geology, Mineral and Energy Resources, Félix Houphouet Boigny University, Abidjan, Côte d’Ivoire. GSC Advanced Research and Reviews, 2025, 24(03), 105–112 Publication history: Received on 02 August 2025; revised on 10 September 2025; accepted on 12 September 2025 Article DOI: https://doi.org/10.30574/gscarr.2025.24.3.0270 Abstract Recharge estimation is a significant concern in any development project utilizing groundwater resources. In this study, groundwater recharge and recharge coefficient were determined using empirical methods applicable to tropical areas. Climatological data from January 2000 to December 2020 were collected at the Daloa synoptic station and were used to estimate the recharge of the area over this period. Using these empirical formulae, the results showed that groundwater recharge was on average 46.19 mm per year, actual evapotranspiration was 958.5 mm per year, and the recharge coefficient was 4.94% for the study area. The results also showed that about 3.86% of precipitation infiltrates the aquifer, 80% is lost through evapotranspiration, and 16.14% as surface runoff. The correlation between climatic parameters and groundwater recharge showed the highest correlation with rainfall. Minimum temperature, relative humidity and evapotranspiration have correlations with recharge of 0.159, 0.225 and 0.028, respectively. The results of the linear regressions showed that precipitation has a significant effect (R2 = 0.9958) on recharge. Keywords: Aquifer; Evapotranspiration; Groundwater Recharge; Recharge Coefficient; Tropical Zone 1. Introduction Groundwater, as a dynamic system, moves under the control of many factors, which are influenced by forces that depend on hydrogeology, hydrology and climatology [1]. In the Lobo catchment, in recent years, the adverse effects of climate change and high population growth have put great pressure on the water resources available to the population [2]. Thus, groundwater has become a key alternative resource in this region. This rush of populations to groundwater requires sustainable management of this resource, which involves controlling the renewal of this resource [3]. Indeed, the renewal of groundwater is an essential factor in any sustainable development programme based on its exploitation. This renewal is called recharge. It is the flow of water that replenishes or replenishes an aquifer, mainly by percolation through the soil [4]. Recharge can be done vertically or laterally from another aquifer system. Recharge, as one of the factors controlling groundwater status and fluctuation, is an important parameter that needs to be further assessed [5]. Recharge, which occurs on both small and large scales, in space and time, is influenced by several factors, such as meteorology, soil characteristics, geology, soil surface cover, slope and depth of groundwater level [6,7,8]. It can be natural and come from precipitation and/or surface runoff, or artificial and come from an intentional supply of water to the soil [8]). The mechanisms governing the natural recharge of aquifers can be described as follows [9]: recharge by vertical percolation of rainfall through the unsaturated zone, known as direct recharge; recharge from water from other aquifers; and recharge by percolation of water through river beds, known as indirect recharge. This description, which may appear very simplistic, does not reflect all the complexity of the recharging mechanisms that can take place simultaneously. Indeed, the recharge of an aquifer by precipitation is controlled by the complex process of infiltration, GSC Advanced Research and Reviews, 2025, 24(03), 105–112 106 flow in the unsaturated zone and the phenomenon of evapotranspiration [10]. Despite this complexity, an estimate of groundwater recharge is essential for optimal management of this resource. For the estimation of this parameter, several methods such as geological, hydrogeological, geophysical and remote sensing techniques have been applied. [11] as there are no universally accepted standard methods as each method has its own advantages and disadvantages [12]. Estimating groundwater recharge from precipitation is an integral part of hydrology and hydrogeology [13]. Although precipitation is the most important source of groundwater recharge [14], the accuracy of currently available techniques for measuring recharge is not fully acceptable. A comparative analysis of empirical formulas for estimating recharge was carried out [15]. Groundwater recharge was estimated from rainfall data using three empirical formulae [16, 5]. The result revealed that the three formulas used gave comparable results, hence their conclusion that any of these formulas can be used for groundwater recharge estimation. In addition, in order to validate the formulas, recharge was estimated using empirical formulas in several regions (arid, semi-arid and tropical) [17,5,18, 19] and gave rather satisfactory results. Thus, the general objective of this study is to estimate the groundwater recharge of the Lobo catchment using empirical equations. 2. Materials and methods Lobo catchment area is located in central-western Côte d'Ivoire, between 6°05' and 6°55' west longitude and between 6°02' and 7°55' north latitude (Fig. 1). This area is limited to the regions of Haut Sassandra, whose capital is Daloa, and part of Worodougou region (Seguela). Daloa is economic hub of this area. In Nibéhibé, this watershed has a surface area of 7,000 Km². This basin is drained by Lobo River and its main right bank tributary the Dé. Average rainfall over period 1971-2020 is about 1200 mm and average temperature is 25°C. Figure 1 Lobo watershed in Nibéhibé 3. Materials and methods 3.1. Data For this study, meteorological data including minimum and maximum temperature, sunshine hours, relative humidity, wind speed and rainfall were collected at the Daloa synoptic station over the period 2000-2020, i.e. 20 years of meteorological data collected at this station. These data were used to calculate evapotranspiration, recharge coefficient, runoff and recharge using empirical formulae. Potential evapotranspiration was determined using CROPWAT version 8.0 based on the Penman-Moteithe FAO formula. GSC Advanced Research and Reviews, 2025, 24(03), 105–112 107 3.2. Methodology 3.2.1. Determination of evapotranspiration Evapotranspiration is regarded as the sum of evaporation and transpiration. It is a climatic index integrating the effect of air temperature, humidity, wind speed, and solar radiation. CROPWAT software version 8.0 was used to estimate evapotranspiration. The software is based on the FAO Penman-Monteith formula [20]. 3.2.2. Estimation of Groundwater Estimation of groundwater recharge of the study area was conducted using a modified version of for tropical regions based on water level fluctuation and rainfall depth [21, 5]. The equation is given as: 𝑅 =1.35(𝑃−14)0.5 (1) where R is the net recharge due to precipitation in mm, and P is the precipitation in mm. 3.2.3. Recharge Coefficients The value of the recharge coefficient is defined as the ratio of recharge to effective rainfall; it is expressed in percentage [21] as: 𝑹𝒄𝒐𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 =𝑹 𝑷𝒆 (𝟐) where R is the recharge and Pe is the effective rainfall. 3.2.4. Estimation Direct Runoff Estimated runoff for water budget was developed by [22], and is given as 𝑹𝒐𝒇𝒇 =𝟎.𝟖𝟓×𝑷−𝟑𝟎.𝟓 (𝟑) where Roff is the direct runoff and P is the precipitation. 3.2.5. Co-Integration Analysis The climatic data was analyzed using co-integration analysis. The analysis involves a unit root test performed on levels, the first difference and the second difference were used to determine whether the individual input series are stationary and exhibit similar statistical properties. It must be noted that regressing a non-stationary time series data over another non-stationary time series data gives a spurious or unreliable regression. To correct for this, a unit root test is performed [23]. A time series is stationary when X(t1), X (t2) …, X(tn) is the same as the joint distribution of any set of X’s(t1+k), X(t2+k) …, X(tn+k), for all n and k. 𝒀𝒕=𝝆𝒀𝒕−𝟏 +𝑼𝒕 (𝟒) where p ≤ 1 and U is the white noise error. If the estimated 𝜌 is statistically equal to 1 when Yt is regressed on Yt−1, then Yt is non-stationary; that is, has no unit root (I (0)). The Augmented Dickey–Fuller (ADF) test was used to test for the stationarity of the data. The test consists of the following regression ∆𝒀𝝉=𝜷𝟏+𝜷𝟐𝝉 +𝜹𝒀𝝉−𝟏𝜶∑∆𝒀𝝉+𝜺𝝉 𝒏 𝝉=𝟏 (𝟓) were GSC Advanced Research and Reviews, 2025, 24(03), 105–112 108 𝜺𝝉=∆𝒀𝒕−𝟏 =(𝒀𝒕−𝟏 −𝒀𝒕−𝟐),∆𝒀𝒕−𝟐 =(𝒀𝒕−𝟐 −𝒀𝒕−𝟑) (𝟔) The Johansen procedure was used to test for the number of co-integration vectors in the model. The Johansen technique was used not only because it is vector auto-regressive-based but because it performs better in multivariate models [24]. If Xt and Yt are then co-integrated, their short-run dynamics can be described by an error correction model (ECM). The theory states that if two variables, Y and X, are co-integrated, then the relationship between them can be expressed as an ECM [24]. The co-integration model is given as: 𝑷𝒕=𝜷𝟎+𝜷𝟏𝑹𝒕+𝜷𝟐𝑺𝑹𝒕+𝜷𝟑𝑴𝑻𝒕+𝜷𝟒𝑿𝑻𝒕+𝜷𝟓𝑬𝑻𝒕+𝑼𝒕 (𝟕) 3.2.6. Correlation and Regression Analyses Pearson correlation coefficient was used to evaluate the strength of the relationship between meteorological factors. The impact of independent variables (that is, humidity, temperature, rainfall, wind speed, and duration of daily solar radiation) on estimated recharge was evaluated using linear regression 4. Results and Discussion All parameters presented in Table 1 were calculated over the period 2000-2020, i.e. a period of 20 years. These parameters are: groundwater recharge, evapotranspiration, recharge coefficient, and surface runoff. The ratio of recharge to total rainfall is 3.86%, which according to [21] is classified as a low recharge rate. The average precipitation and evapotranspiration over the study period are estimated at 1197.72 mm and 1357.8 with lower and upper quartiles of 1379; 1081.8 for precipitation and 1495.12; 1234.17 for evapotranspiration. There is evidence that apart from precipitation (with a high rate), other parameters (such as land use) could also have a key role [25] in groundwater recharge. Table 1 Hydrological data of the study area for 20 years (2000 to 2020) Years P (mm/y) R (mm/y) Max T °C Min T °C ET (mm/y) Roff Roe(%) 2000 1329.1 48.96 37.04 16.89 1291.34 1099.24 4.62 2001 841.2 38.83 37.63 19.30 1343.94 684.52 5.78 2002 1214.4 46.77 36.80 20.51 1476.72 1001.74 4.83 2003 1379 49.88 36.48 20.48 1847.42 1141.65 4.53 2004 1242 47.31 35.48 20.66 1693.69 1025.20 4.77 2005 1152.5 45.55 34.09 20.25 982.26 949.13 4.95 2006 1129.1 45.08 32.70 19.56 1367.90 929.24 5.00 2007 1133.9 45.18 32.26 19.78 983.39 933.32 4.99 2008 1443.5 51.04 33.51 19.81 1620.80 1196.48 4.43 2009 1264.4 47.74 33.60 19.79 1529.83 1044.24 4.73 2010 1412 50.48 34.73 20.11 1284.45 1169.70 4.48 2011 981.6 41.99 34.84 16.45 1301.49 803.86 5.36 2012 1440 50.98 34.14 19.86 1315.87 1193.50 4.44 2013 1614 54.00 32.75 19.84 1495.12 1341.40 4.19 2014 1594.9 53.68 36.24 20.16 1789.69 1325.17 4.22 2015 1009.3 42.59 34.13 16.50 1345.67 827.41 5.29 2016 1188.2 46.26 33.65 20.01 982.32 979.47 4.88 2017 722.6 35.94 34.78 19.89 1325.85 583.71 6.23 2018 1081.8 44.11 34.89 20.21 1113.92 889.03 5.11 GSC Advanced Research and Reviews, 2025, 24(03), 105–112 109 2019 1237 47.21 35.08 16.55 1188.08 1020.95 4.78 2020 741.6 36.41 37.41 20.06 1234.17 599.86 6.15 Average 1197.72 46.19 34.87 19.37 1357.8 987.56 4.94 Maxi 1614 54 37.63 20.66 1847.42 1341.4 6,23 Mini 722.6 35.94 32.26 16.45 982.26 583.71 4,19 Q (25%) 1081.8 44.11 33.65 19.56 1234.17 889.03 4.53 Q (75%) 1379 49.88 36.24 20.16 1495.12 1141.65 5.11 In Table 1, P is the annual precipitation, R is the annual groundwater recharge (mm), ET is the evapotranspiration (mm/year), MinT is the minimum temperature (°C), MaxT is the maximum temperature (°C), Roff et the surface runoff (mm/y) and Rcoeff. is the recharge coefficient (%). Q: Quartile Fig. 2 shows the evolution of mean annual precipitation, runoff, evapotranspiration and recharge between 2000 and 2020 in the study area.These parameters (groundwater recharge and surface runoff) increase and decrease with precipitation over the entire study period. However, evapotranspiration shows another trend which is different from the other parameters. This is not unexpected since ET is a function of solar radiation, daily dew point wind speed and temperature [21,5]. Indeed, over the period, these parameters, mainly temperature, have increased. It should be noted that low rainfall in the study area leads to low groundwater recharge and vice versa. This decrease in rainfall over the period 2000-2020 could be due to the adverse effects of climate change which could lead to increased temperature and evapotranspiration in the area [2] . Figure 2 Total annual precipitation, recharge, runoff and evapotranspiration 4.1. Co-integration Table 2 shows the results of the ADF test. These results show that recharge and precipitation are non-stationary, but are stationary after the second differentiation. The other climate parameters are non-stationary. It was verified that the variables have a unit root at their levels, but are stationary after differentiation, so there is a long term relationship between the climate parameters. These observations were also made by [5] in a similar environment. GSC Advanced Research and Reviews, 2025, 24(03), 105–112 110 Table 2 Result of stationary test from the Augmented Dickey-Fuller Test Climate parameters Level 1st Difference 2nd Difference Decision Precipitation -3.454448 -6.542228 -5.315195 Stationary Recharge -3.524826 -6.658083 -5.442094 Stationary Evapotranspiration -4.013366 -6.164948 -6.960297 Non-Stationary Max temperature -2.000817 -4.726188 -5.739369 Non-Stationary Min temperature -1.237497 -6.630274 -4.156138 Non-Stationary Relative Humidity -2.232637 -4.287431 -3.959210 Non-Stationary Critcal level (5%): -3.808546 Table 3 presents the results of Johansen's co-integration. The likelihood ratio shows that there is only one cointegration equation in the analysis. According to the equation, the precipitation variable precipitation was considered to have a significant effect on the estimated recharge in the study area during the study period. Table 3 Johansen co-integration result Hypothèsised No. of CE (s) Eigen Value Max-Eigen statistic 0.05 Critical Value Prob** None* 0.991926 91.56295 40.07757 0.0000 At most 1 0.706763 23.30872 27.58434 0.1607 At most 2 0.432674 10.76961 21.13162 0.6703 At most 3 0.353743 8.294612 14.26460 0.3495 At most 4 0.032378 0.625368 3.841465 0.4291 The adjusted R2 shows that about 91% of the variation in recharge is explained by rainfall effects (Table 4). This shows that an increase in precipitation value leads to an increase in recharge value and vice versa. Table 4 Results from the Error Correction Model Variable Coefficient Standard. Error t-Statistics Probability Recharge -7.678405 1.365982 -5.482105 0.0003 Evapotranspiration -0.043558 0.055815 -0.780404 0.4448 Relative Humidity -0.000730 0.003615 -0.202026 0.8410 MaxTemperature -0.000151 0.008734 -0.017312 0.9864 Min Temperature -0.004451 0.003615 -0.202026 0.8420 Depend vatiable : precipitation. R² = 0.9148; Durbin-watson stat: 2.133321 4.2. Correlation and Regression Analysis Tables 5 and 6 show the Pearson correlation coefficient for the meteorological parameters and the regression analysis of these parameters on the estimated recharge in the study area. This analysis was done in order to study the correlation between the estimated recharge and the climatic parameters. The results show that evapotranspiration is correlated with estimated recharge at the significance level of 0.028, with R2 = 0.0178. The estimated recharge is correlated with precipitation at the 0.998 threshold (R2 = 0.998), but shows no significant correlation with other parameters. This would mean that rainfall is the only climatic parameter that has a significant impact on groundwater recharge in our study area. Thus, an increase in rainfall leads to an increase in groundwater recharge and vice versa [5]. GSC Advanced Research and Reviews, 2025, 24(03), 105–112 111 Table 5 Matrice de correlation (Pearson) Variables Precipitaion Recharge Max T Min T ET Relative Humidity Precipitatio 1 0.998 -0.245 0.174 0.074 0.223 Recharge 0.998 1 -0.259 0.159 0.028 0.225 Max T -0.245 -0.259 1 -0.043 0.245 0.130 Min T 0.174 0.159 -0.043 1 0.174 -0.106 ET 0.074 0.028 0.245 0.174 1 0.023 Relative Humidity 0.223 0.225 0.130 -0.106 0.223 1 Values in bold are different from 0 at significance level alpha=0.05 Table 6 Regression analysis of weather parameters on estimated recharge Weathered Parameter / Recharge Determination Coefficient (r 2 ) Regression Equation Precipitation 0.9958 Y=0.0202x+21.997 Max Temperature 0.0545 Y=0.3953x+39.544 Min Température 0.0245 Y=0.5639x+35.27 Relative Humidity 0.12572 Y=0.8749x+61.049 Evapotranspiration 0.0178 Y=25.038x+206.06 5. Conclusion The application of empirical methods for the estimation of groundwater recharge in the study area has made it possible to estimate this parameter over the period 2000-2020. The average recharge over this period is 46.9 mm. The average recharge coefficient is 4.94%, the evapotranspiration rate is about 80%, and the groundwater recharge is about 3.86%. Therefore, 16.14% of the rainwater contributes to surface runoff. The study shows that there is variability in climate parameters and that climate has a significant effect on groundwater resources. This is clearly revealed in the precipitation variable; however, evapotranspiration and temperature have a relationship with each other. The study also shows that climate change has a significant effect on recharge, clearly revealed by the variability of rainfall. The correlation between the parameters showed that the highest positive correlation exists between recharge and rainfall. Furthermore, the correlations between min temperature, relative humidity, and ET and estimated recharge are 0.159; 0.225 and 0.028. The results of the linear regressions showed that precipitation has a significant effect (with R2 = 0.9958) on the recharge estimate. Compliance with ethical standards Acknowledgments We would like to thank the team at the Environmental Science and Technology Laboratory at Jean Lorougnon Guédé University and all the authors for their contributions to the fieldwork and writing of this study. Disclosure of conflict of interest The authors declare that they have no conflict of interest. References [1] Kumar CP. Estimation of natural ground water recharge. ISH J. Hydraulic Eng. 1977; 3: 61–74. [2] Koffi B, Sanchez M, Kouassi KL, Kouadio ZA, Kouassi KH, Yao AB. (2021) Evaluation of the impacts of climate change and land-use dynamics on water resources: The case of the Lobo River watershed: Central-Western Côte d'Ivoire. EGU General Assembly 2021 Author(s) 2021, EGU21: Gather Online; EGU: France, 2021; pp. 21-506. GSC Advanced Research and Reviews, 2025, 24(03), 105–112 112 [3] Ali Rahmani SE, Chibane B, Boucefiène A. Sensitive analysis of ground recharge estimation model, for semiarid areas. Applied Water Science. 2018; 193: 1-10. [4] Soro DD. Characterization and hydrogeological modeling of an aquifer in a fractured bedrock environment: case of the Sanon experimental site (central plateau region of Burkina Faso) [Doctoral thesis] Pierre and Marie Curie University–Paris 6 (UPMC): International Institute of Water and Environmental Engineering (2iE); 2017. [5] Adeleke OO, Makinde V, Eruola OA, Dada OF, Ojo AO, Aluko TJ. Estimation of groundwater recharges in Odeda local government area, Ogun state, Nigeria using empirical formulae. Challenges. 2015; 6 (2): 271–281. [6] Bouwer H. Groundwater Hydrology; McGraw-Hill Book Company: New York, NY, USA ;1978. [7] Sumioka SS, Bauer HH. Estimating ground-water recharge from precipitation on Whidbey and Camano Islands, Island County, Washington, water years 1998 and 1999. In Water-Resources Investigations Report. 2003; 034101. [8] Bhunia G S. An approach to demarcate groundwater recharge potential zone using geospatial technology. Applied Water Science. 2020; 10 :138. [9] Kingumbi A. Hydrological modeling of a basin affected by occupation changes: case of Merguellil in central Tunisia. [Doctoral thesis]. Tunisia: University of Tunis El Manar; 2006. [10] Healy RW, Cook PG. Using groundwater levels to estimate recharge. Hydrogeology Journal. 2002; 10 (1): 91–109. [11] Yeh HF, Cheng YS, Lin HI, Lee CH. Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustain Environ Res. 2016; 26(1):33–43. [12] Risser DW, Gburek WJ, Folmar GJ. Comparison of recharge estimates at a small watershed in east-central Pennsylvania, USA. Hydrogeol. J. 2008; 17: 287–298. [13] Xi C, Zhang Z, Zhang X, Chen Y, Qian M, Peng S. Estimation of groundwater recharge from precipitation and evapotranspiration by Lysimeter measurement and soil moisture model. J. Hydrol. Eng. 2008; 13: 333–340. [14] Kumar CP, Seethapathi PV. Assessment of natural ground water recharge in upper Ganga Canal command area. J. Appl. Hydrol. 2002; 15: 13–20. [15] Oke MO, Martins O, Idowu O, Aiyelokun, O. Comparative analysis of empirical formulae used in groundwater recharge in Ogun-Oshun River Basins. J. Sci. Res. Repo. 2013; 2: 692–710. [16] Kumar CP.Groundwater Assessment methodology; National Institute of Hydrology: Roorkee, India; 2009. [17] Saghravani SR, Ismail Y, Sa’ari M, Saghravani SF. Estimating groundwater recharge using empirical method: A case study in the tropical zone. Sains Malaysiana .2013; 42: 553–560. [18] Ali Rahmani SE, Chibane B, Boucefiène A. Groundwater recharge estimation in semi-arid zone: a study case from the region of Djelfa (Algeria). Appl Water Sci. 2016; 7: 2255-2265. [19] Hung V V, Broder J M. Estimating groundwater recharge for Hanoi, Vietnam. Science of the Total Environment. 2018; 651: 10471057. [20] llen R, Pereira LA, Raes D, Smith M. Crop evapotranspiration. In FAO Irrigation and Drainage Paper No. 56; FAO: Rome, Italy, 1998. [21] Misstear BDR, Brown L, Daly D. A methodology for making initial estimates of groundwater recharge from groundwater vulnerability mapping. Hydrogeol. J. 2009; 17: 275–285. [22] Muteraja K N. Applied Hydrology; Tata MGraw-Hill: New Delhi, India, 1986. [23] Ayinde E O, Muchie M, Olatunji GB. Effect of climate change on Agricultural productivity in Nigeria: A cointegration model approach. J. Hum. Ecol. 2011; 35: 189–194. [24] Maddala GS. Introduction to Econometrics, 3rd Ed.; John Wiley and Sons Ltd: New York, NY, USA; 2001. [25] Shukla S.; Jaber FH. Groundwater Recharge from Agricultural Areas in the Flatwoods Region of South Florida. Available online; 2015.