The Sassandra Basin, like most regions of Côte d’Ivoire, is increasingly affected by droughts that involve many environmental, social and economic impacts. This basin is full of several amenities such as hydroelectric dams, hydraulic and agricultural dams. There is also a strong agricultural activity. In the context of climate change, it is essential to analyze the occurrence of droughts in order to propose mitigation or adaptation measures for water management. The methodological approach consisted initially in characterizing the dry sequences by the use of the SPI (Standardized Precipitation Index) and secondly in determining the probabilities of occurrence of successive dry years using by Markov chains 1 and 2. The results indicate that most remarkable droughts in terms of intensity and duration occurred after the 1970s. A comparison of Markov matrices 1 and 2 between the period considered 1953–2015 with the periods 1953–1970 and 1971–2015 shows a profound change in the distribution of droughts at the different station. Thus, the probability of having two successive dry years is greater over the period 1970–2015 and is accentuated to the Southern and Northern regions (probabilities ranging from 71% to 80%) of the basin. Over the 1970–2015 period, the probability of obtaining three successive dry years is significantly high in this watershed (between 20% and 70%).

In view of the complexity of the phenomenon of water related soil erosion in the Bia catchment area, linked to a large heterogeneity of soils, to a very scattered and in some places non-existent vegetation cover, and to a poorly distributed precipitation in both space and time, a mapping test of the “specific erosion” random variable is undertaken. The mapping of the intensity of the erosion hazard was carried out using the Universal Soil Loss Model (USLE). The map shows that the basin is generally characterized by relatively moderate erosion rates with an average erosion rate of 16 t/ha/year.

The floods that have affected many African countries south of the Sahara have not exempted Ivory Coast, especially since the 1970s. The impact of these events has led to major climatic disruptions, particularly the occurrence of extreme rainfall. In the Bandama catchment area, in September 2018 alone, the heavy rains that fell led to a flooding of the Bandama River. The towns of Zenoula and Bouaflé were flooded, plantations destroyed. According to the Agence de Presse Ivoirienne (AIP) on 22 September 2018, a total of 2017 people including 1130 children are affected in Bouaflé. In this context, it is important to better understand how the irregularity and distribution of extreme rainfall occurs in order to adopt preventive measures. However, the lack of studies on climate trends does not allow us to anticipate environmental problems. Indeed, the simplest way to understand this variability, although the necessary concepts and notions must be available, is to consider that there is an elementary and scale-invariant process that reproduces this variability from scale to scale. The possibility of characterising and modelling the variability of precipitation from scale to scale rather than at a given scale has led to a number of studies on multifractal precipitation (Lovejoy et al., 1987; Hubert et al., 2002). The objective of this study is to highlight the occurrence of extreme rainfall in the Bandama catchment using multifractal analysis. For this work, daily data from 2005 to 2010 from 10 stations (Yamoussoukro, Bouaflé, Dimbokro, Ferkessedougou, Niakara, Séguela, Bouaké, Tiassalé, Grand-Lahou, Zuenoula) were considered. Three intensity thresholds (with respective values of 20; 35; 50 mm/d) were studied. For these thresholds, the respective fractal dimension is 0.321; 0.249; 0.232.  The probability of obtaining a rainy day with thresholds set at 50 mm, 35 mm, 20 mm is respectively 0.069; 0.074; 0.093 over one month. The probability of obtaining rainy days for the three thresholds is spatialized over the catchment.Keywords: Occurrence of extreme rainfall, multifractal, Bandama basin, Ivory Coast

The Comoé Estuary, located in Grand-Bassam, Côte d'Ivoire, is a coastal ecosystem of major ecological and socio-economic importance. Since the permanent opening of its mouth in September 2023, the estuary has experienced hydrological and environmental disturbances whose magnitude remains poorly documented. This study adopts an integrated approach combining morphobathymetric characterization and the analysis of the spatial distribution of water physicochemical parameters. A sonar survey conducted on January 3, 2026, yielded 44,097 bathymetric sounding points. Five physicochemical parameters were measured in situ at 95 stations on February 28, 2026. Results reveal a heterogeneous estuarine bed, with depths ranging from 0 to 17 m (mean: 4 m), and two types of morphological profiles. V-shaped profiles indicate dominant erosion, whereas intermediate profiles reflect an unstable balance between erosion and sedimentation. These findings point to a mixed and unstable depositional environment characterized by continental inputs and marginal marine conditions. From a physicochemical perspective, pH values (7.13-8.38) indicate a generally alkaline environment. Conductivity (17.43-55.20 µS/cm) and salinity (0.968-3.59‰) reflect a strong marine influence downstream, while dissolved oxygen concentrations (0.10-0.30 mg/L) reveal a state of severe hypoxia, representing the most critical ecological constraint of the system. Pearson correlation analysis confirms a very strong relationship between conductivity and salinity (r = 0.998), as well as strong negative correlations between temperature and mineralization-related parameters. These correlations indicate that water has become a vector of persistent pollution that weakens the ecosystem and threatens the food security and health of local populations. The environment is progressively losing its capacity for regeneration, and the impacts may be transmitted across generations.

PROBLEM STATEMENT Recent flooding in the Bia watershed has affected the Ayamé 1 and 2 hydroelectric dams functionnality and caused damage in the Aboisso area. Studies have revealed that the threshold for observing these floods is estimated at 100 mm of rainfall. It therefore appears necessary to verify the link between the 100 mm rainy days trend and the floods recurrence on the Bia basin. OBJECTIVE This study aims at analyzing the trend of rain over a threshold 100 mm in the Bia watershed. METHODOLOGY The frequency of rainy days over a threshold 100 mm was constituted from 1941 to 2015 from the daily rainfall available over the same period at the stations Adiaké, Bianouan, Aboisso, Ayamé and Agnibilékro. The linear regression between these rainfall frequencies and time allowed us to assess the trend of rainfall days over threshlod 100 mm at each station. The slope of the regressions allowed us to appreciate the increasing or decreasing trends while the the t-test probability p applied to the regression slope highlights the highly significant (p < 0.01), significant (0.01 ≤ p <0.05) and non-significant (p ≥ 0.05) trends. RESULTS The daily rainfall over threshold the 100 mm frequency varies from 15 days in Agnibilékro to 120 days in Adiaké. The trend in these rainfall thresholds is characterized by an average decrease of 0.5% per decade. On an interannual scale, this negative trend was very significant in Adiaké and Bianouan, then significant in Aboisso and insignificant in Ayamé and Agnibilékro. An intermonthly observation, the decrease was very significant in May, June and July in Aboisso, Adiaké and Bianouan respectively. CONCLUSION The flooding recurrence in the Bia basin is not related to an increase in the frequency of threshold rains (100 mm) of flooding, it could be related to the vulnerability of the basin.

The streamflow is important for planning et decision-making of development activities  related to water resource. The hydrological mathematical models are frequently used to simulate the transformation of rainfall into streamflow as well as Artificial Neural Networks (ANNs. In order to contribute to the establishment of an adequate framework for water resource management in the Davo river basin, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis. The Davo River basin is located in the southwestern forest of Côte d'Ivoire. The hydrological models used are the HEC-HMS model (Hydrologic Engineering Center-Hydrologic Modeling System) and the GR4J model (Rural Engineering with four daily parameters), while the data-driven ANN model is developed in MATLAB. The three tools receive the same data and their parameters are calibrated using the same objective functions. The quality of the simulations is measured in the control phase using several statistical criteria. For the assesment of the impacts of climate change on hydrological regime of the Davo river basin, the AFRICA-CORDEX (Coordinated Downscaling Experiment for Africa) data under two RCP (representative concentration pathway) scenarios   RCP4.5 and RCP8.5 are integrated in the three tools of modelling. The approach developed in this study allows a considerable improvement of the outils performances developed in calibration and validation. In addition, the results of the current study provide valuable feedback for water resources’ planners in Côte d’Ivoire and the developing regions.Keywords: Climate change, Streamflow, Artificial Neural Networks, HEC-HMS, GR4J, Davo river basin, Côte d’Ivoire