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We examined whether mobile pastoralists in the Logone floodplain of Cameroon distribute themselves according to the ideal free distribution (IFD), which predicts that the number of individuals in each area is proportional to the quality and quantity of resources in each area and that all individuals have access to the same amount of resources. We used the concept to assess the distribution of grazing pressure over available common-pool resources as evidence of a complex adaptive system in which the spatial distribution grazing pressure is adjusted to the distribution of resources through individual decision making and passive coordination of movements among individual pastoralists. We used a combination of spatial and ethnographic approaches to study the distribution of resources and mobile pastoralists in the Logone floodplain in 5 successive years and found evidence for an IFD in 3 years (2008–2009 and 2012) and an approximation of an IFD in years in which pastoralists were terrorized by armed bandits (2010) and the government reestablished security (2011). The findings support our hypothesis that there is a self-organizing management system in which pastoralists distribute themselves effectively over the available resources.

The objective of this study is to investigate the effects of rainfall variability and anthropogenic changes on river discharge in the Benoue and the Logone river basins over the last seven decades (1950–2018). To achieve this goal, hydrometeorological data from these basins were analyzed using the Pettitt and Mann–Kendall tests. Our results show that negative rupture was observed in the hydrometeorological time series of these basins at the annual time step in 1970–1971. The deficits associated with this rupture are estimated at −7% for rainfall and −28% for river flows. The wet season shows similar developments. However, from the 1990s onward, there has been a significant increase in the mean annual flows of the Benoue River, which coincides with that of the rainfall during the same decade. This increase over the recent decades could also be expected in response to an increase in impervious surface area in the catchment area, which could compensate for the deficit generated by the post-1990s rainfall deficit through an increase in runoff. Since the filling of the Lagdo Dam in 1983, an increase in all ranges of minimum flow, as well as an increase in the variability of extreme flows, has been detected.

Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R2) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed streamflow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out-performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.