A selected number of global climate models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) were evaluated over the Volta Basin for precipitation. Biases in models were computed by taking the differences between the averages over the period (1950–2004) of the models and the observation, normalized by the average of the observed for the annual and seasonal timescales. The Community Earth System Model, version 1-Biogeochemistry (CESM1-BGC), the Community Climate System Model Version 4 (CCSM4), the Max Planck Institute Earth System Model, Medium Range (MPI-ESM-MR), the Norwegian Earth System Model (NorESM1-M), and the multimodel ensemble mean were able to simulate the observed climatological mean of the annual total precipitation well (average biases of 1.9% to 7.5%) and hence were selected for the seasonal and monthly timescales. Overall, all the models (CESM1-BGC, CCSM4, MPI-ESM-MR, and NorESM1-M) scored relatively low for correlation (<0.5) but simulated the observed temporal variability differently ranging from 1.0 to 3.0 for the seasonal total. For the annual cycle of the monthly total, the CESM1-BGC, the MPI-ESM-MR, and the NorESM1-M were able to simulate the peak of the observed rainy season well in the Soudano-Sahel, the Sahel, and the entire basin, respectively, while all the models had difficulty in simulating the bimodal pattern of the Guinea Coast. The ensemble mean shows high performance compared to the individual models in various timescales.

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky–Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem–atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

Methane (CH₄) is a major and potent greenhouse gas (GHG), and its emissions from agricultural activities, particularly rice cultivation, are a significant concern for climate change. Due to the high demand for food security, driven by rapid population growth and national initiatives to reduce dependency on rice imports, rice cultivation is intensified in West Africa. However, its contribution to atmospheric CH₄ remains largely unknown. Here, for the first time, cutting-edge eddy covariance tower measurements were conducted parallelly in a rice field (Janga) and a reserve forest (Mole National Park), both located in the Guinea savanna region of West Africa. Using CH₄ measurement data from June to October 2023 (rice cultivation period), the dynamic interplay between methane emissions from rice cultivation and its potential mitigation through forest methane uptake was assessed. Our results show that the rice field acted as a net source of CH₄ at a rate of 2037 mgCH₄m⁻², whereas the most intense flooded period (August) accounted for 70% of the total emissions. On the other hand, the forest reserve acted as a sink, with a net uptake of −560 mgCH₄m⁻², and the highest uptake observed in October. Accounting for the global warming potential (GWP) of CH₄ over a 20 year period, the forest had a wet season negative GWP of −47.04 gCO₂eq, while the rice field emitted CH₄ of 171.36 gCO₂eq. This implies that under similar conditions during the measurement campaigns, the forest per square area needs approximately a factor of ∼4 to balance the positive radiative effect per square area of rice cultivated. This work emphasizes the need to integrate forests to compensate for methane released by rice cultivation in the semi-arid West African savannah region.

This study investigated the perception of drought by cocoa farmers and explored the effectiveness of adaptive strategies (ASs) used in smallholding farms in the transboundary region between Ghana and Togo. Drought significantly threatens cocoa production in this region, affecting farmers’ livelihoods and cocoa supply chains. This study used a multistage sampling approach, which involved surveys with questionnaires administered to 330 cocoa farmers throughout the study area, along with on-site observations. Statistical analysis included binary logistic and Poisson regression models to explore the relationship between farmer socioeconomic characteristics and adaptation practices. The findings revealed that cocoa farmers in the region have a nuanced understanding of drought, attributed to changing climatic patterns and unsustainable land management practices such as deforestation. To mitigate its impacts, farmers employ a variety of ASs, including investment in farm management, soil management, and intercropping with crop diversification. Furthermore, socioeconomic factors, including age, formal education, household size, land tenure right, adaptation cost assessment, and an underestimation of self-efficacy, were shown to affect the choice in the AS. Among the ASs adopted, only farm management practices (weeding, pruning, fertilizer application, etc.) significantly improved the cocoa yield. This study contributes to understanding drought as a critical issue for cocoa farmers and the adaptation practices used by smallholder cocoa farmers. Given that among the strategies adopted, only farm management practices, also known as good agricultural practices (GAPs), significantly improves yield, this study recommends well-designed and innovative packages of sustainable farm management based on farm and owner characteristics. These include irrigation schemes, timely soil fertilizer monitoring and supply, and the provision of drought-resistant varieties along with technical itineraries. Additional interventions require drought emergency responses, with other factors such as education and financial support mechanisms expected to improve farmers’ timely decision-making to adapt and improve cocoa production resilience to drought episodes in international transboundary regions with complex governance structures.

To understand surface energy exchange processes over the semiarid regions in West Africa, numerical simulations of surface energy and water balances were carried out using a one-dimensional multilayer atmosphere-SOil-VEGetation (SOLVEG) model for selected days of the dry and rainy seasons over a savanna grassland ecosystem in Sumbrungu in the Upper East region of Ghana. The measured Bowen ratio was used to partition the residual energy into the observed sensible heat flux (H) and latent heat flux (LE) in order to investigate the impact of the surface energy closure on model performance. The results showed that the model overall reproduced the diurnal changes in the observed energy fluxes, especially the net radiation (Rn), compared to half-hourly eddy covariance flux measurements, for the study periods. The performance measure in terms of the correlation coefficient (R), centred root mean square error (RMSE), and normalized standard deviation (σ) between the simulated H and LE and their corresponding uncorrected observed values ranged between R = 0.63–0.99 and 0.83–0.94, RMSE = 0.88–1.25 and 0.88–1.92, and σ = 0.95–2.23 and 0.13–2.82 for the dry and rainy periods respectively, indicating a moderate to good model performance. The partitioning of H and LE by SOLVEG was generally in agreement with the observations during the dry period but showed clear discrepancies during the rainy period, particularly after rainfall events. Further sensitivity tests over longer simulation periods (e.g., 1 year) are required to improve model performance and to investigate seasonal exchanges of surface energy fluxes over the West African Savanna ecosystems in more details.