Impact of Climate Variability on Water Resources: The Case of Marc Delorme-Cnra Station, Southeast of Ivory Coast

This study aims to characterize the climatic variability in the South-East of Ivory Coast and to show its impact on the supply of water resources. To do this, statistical and hydrological methods were applied to climatic data collected at the Marc DELORME Research Station of the CNRA. The statistical trend tests on this data revealed a significant decrease in precipitation and an increase in temperature, insolation and evaporation. Statistical break methods indicate a rainfall break in 1982 which marks a modification of the rainfall regime thus translating a drop in rainfall of 15%, a recession in the frequency of rainy days in general and in particular in rainfall heights between 10 and 30 mm and greater than 50 mm. This break is accompanied by a shortening of the rainy seasons, with average rainfall durations ranging from 54 days (short rainy season) to 104 days (great rainy season). Despite the disturbances in the different seasons of the year, the monthly rainfall regimes in the area have not changed. The assessment of the effects of drought on water resources using the Standardized Precipitation and Evapotranspiration Index (SPEI) for three-time scales (1 month, 3 months and 12 months) indicates a severe drought ranging from 3% to 7% over the period 1961 to 2018. However, despite the presence of this severe drought, the intensity of the drought was found to be moderate on all time scales. The Thorrnthwaite method was used to highlight the impacts of this climatic variability on the region’s water resources. The average annual recharge estimated at 402 mm, has been reduced to 153 mm during a deficit period, a decrease of about 62%. The average annual runoff, which was 294 mm, fells to 257 mm, a decrease of about 13%. This recorded decrease in the water infiltrated after the rainfall break (1983-2018), explains the heterogeneous decrease in the depth of the water table.

Semi-Automatic Fracture Mapping Using Cellular Neural Networks Applied to ALOS PALSAR 2 Images of the Western Highlands of Cameroon

In Cameroon in general and in the Highlands of Cameroon in particular, there is no fracture map since its realization is not easy. The region’s harsh accessibility and climatic conditions make it difficult to carry out geological prospecting field missions that require large investments. This study proposes a semi-automatic lineament mapping approach to facilitate the elaboration of the fracture map in the West Cameroon Highlands. It uses neural networks in tandem with PCI Geomatica’s LINE algorithm to extract lineaments semi-automatically from an ALOS PALSAR 2 radar image. The cellular neural network algorithm of Lepage et al (2000) is implemented to enhance the pre-processed radar image. Then, the LINE module of Geomatica is applied to the enhanced image for the automatic extraction of lineaments. Finally, a control and a validation of the expert by spatial analysis allows elaborating the fracture map. The results obtained show that neural networks enhance and facilitate the identification of lineaments on the image. The resulting map contains more than 1800 fractures with major directions N20° - 30°, NS, N10° - 20°, N50° - 60°, N70° - 80°, N80° - 90°, N100° - 110°, N110° - 120° and N130° - 140° and N140° - 150°. It can be very useful for geological and hydrogeological studies, and especially to inform on the productivity of aquifers in this region of high agro-pastoral and mining interest for Cameroon and the Central African sub-region.