In several areas, many social, economic, and physical subsystems interact around water resources. Integrated water management is applied to maximize economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems, mainly in hydrologic-stressed areas. The Souss-Massa basin, with its semi-arid climate, has a significant demand for agricultural, industrial, tourism, and domestic water. It constitutes a complex system where the lack of knowledge of all the interacting subsystems has led to a shortage of water in quantity and quality. The objective of this study is to investigate the interactions between supply and demand at different stages using a System Dynamics (SD) approach. The model developed promotes a holistic understanding of the interactions between the different problem indicators that operate in water resources management in order to support decision-making action and successfully manage water resources at the Souss-Massa basin scale. The chosen performance indicator is based on the achievement of a baseline sustainability index (SI) defined as the ratio of available water to supply water that should be higher than 20% to avoid a water stress situation. The multisource data were gathered from different government agencies for the period spanning between 2007 and 2020. The results showed that the current policies do not lead to sustainable water management. Groundwater withdrawals have increased considerably, from 747 Mm³ in 2007 to 4884 Mm³ in 2020. The balance between water supply and demand is only reached for three years, 2010, 2015, and 2018, without ever reaching an SI of 20%. The sensitivity analysis showed that the sustainability of water resources in the Souss-Massa basin is mainly impacted by the availability of surface water, irrigated areas, and irrigation efficiency. This study will be of great interest to policymakers to provide optimal and sustainable water management strategies based on improved water use efficiency, and to contribute to the sustainable development agenda in arid and semi-arid regions.

Africa’s rapidly growing population is exerting a lot of pressure on agricultural resources including maize yields and harvest area. Across Africa, access to advanced options for increasing maize yields are inadequate. This is daunting as most of the cultivation of maize is in the hands of smallholder farmers who have inadequate access to modern methods of farming. This has resulted in an increase in dependency on harvest area to increase yields. However, it is still unclear how this maize-yield-harvest-area dynamic plays out across different regions of Africa. This study uses crop yield and harvest area time series data from FAOSTAT for the period 1961–2019. The data are analyzed using linear interpolation, the normalization technique, the rate of change, the Pearson correlation coefficient, the coefficient of determination and regression analysis. The results show that maize yields and harvest area have increased by 71.35% and 60.12%, respectively across Africa. Regionally, West, Middle and East Africa witnessed a positive relationship between maize yields and harvest area while in North and Southern Africa, maize yields and harvest area have an inverse relationship. For example, in assessing the relationship between maize yield and harvest area in Africa, this work observes that North Africa has a correlation of −35% and an R² of 12%, while Southern Africa has a correlation of −36% and R² of 13%. On the other hand, West Africa has a correlation of 87% and an R² of 76%, while Middle Africa recorded a correlation of 66% and an R² of 42%. East Africa recorded a correlation of 76% and R² of 61%. These results confirm that maize yield and harvest area have a positive relationship in West, Middle and East Africa and a negative relationship in North and Southern Africa. These results underscore the fact that in North and Southern Africa, maize production is less dependent on harvest area as is the case in the other regions of Africa. Such findings have implications for adaptation planning especially in sub-Saharan Africa where food insecurity is closely related to land and forest degradation.

Climate projections in Sub-Saharan Africa (SSA) forecast an increase in the intensity and frequency of droughts with implications for maize production. While studies have examined how maize might be affected at the continental level, there have been few national or sub-national studies of vulnerability. We develop a vulnerability index that combines sensitivity, exposure and adaptive capacity and that integrates agroecological, climatic and socio-economic variables to evaluate the national and spatial pattern of maize yield vulnerability to droughts in Uganda. The results show that maize yields in the north of Uganda are more vulnerable to droughts than in the south and nationally. Adaptive capacity is higher in the south of the country than in the north. Maize yields also record higher levels of sensitivity and exposure in the north of Uganda than in the south. Latitudinally, it is observed that maize yields in Uganda tend to record higher levels of vulnerability, exposure and sensitivity towards higher latitudes, while in contrast, the adaptive capacity of maize yields is higher towards the lower latitudes. In addition to lower precipitation levels in the north of the country, these observations can also be explained by poor soil quality in most of the north and socio-economic proxies, such as, higher poverty and lower literacy rates in the north of Uganda.

Climate change continues to have adverse effects on crop yields in Africa and globally. In Morocco, rising temperatures and declining precipitation are having daunting effects on the vulnerability of crops. This study examines the vulnerability of barley, maize, and wheat to variations in growing season precipitation and socio-economic proxies of adaptive capacity such as literacy and poverty rates at both national and sub-national scales in Morocco. The methodology is based on a composite vulnerability index (vulnerability is a function of exposure, sensitivity, and adaptive capacity). National and sub-national crop yield data used to compute the sensitivity index were downloaded from FAOSTAT and the global crop yield gaps Atlas. The mean annual growing season precipitation data at both the national and sub-national scales used to compute the exposure index were downloaded from the world bank climate portal. Proxy data for adaptive capacity in the form of literacy and poverty rates were downloaded from the world bank, figshare, and MPR archives. The CANESM model was used to validate the crop yield observations. The results show that wheat shows the lowest vulnerability and the highest adaptive capacity, while maize has the highest vulnerability and lowest adaptive capacity. Sub-nationally, vulnerability indexes decrease northwards while adaptive capacity and normalized growing season precipitation increase northwards. Wheat also shows the lowest vulnerability and highest adaptive capacity and normalized growing season precipitation at each latitude northward. Model validation shows that the models used here reproduce most of the spatial patterns of the crops concerned. These findings have implications for climate change adaptation and climate policy in Morocco, as it becomes evident which of these most cultivated crops are more vulnerable nationally and spatially. These results have implications for future research, as it might be important to understand how these crops perform under growing season temperature as well as what future projections and yield gaps can be observed.

In sub-Saharan Africa growing season precipitation is affected by climate change. Due to this, in Cameroon, it is uncertain how some crops are vulnerable to growing season precipitation. Here, an assessment of the vulnerability of maize, millet, and rice to growing season precipitation is carried out at a national scale and validated at four sub-national scales/sites. The data collected were historical yield, precipitation, and adaptive capacity data for the period 1961–2019 for the national scale analysis and 1991–2016 for the sub-national scale analysis. The crop yield data were collected for maize, millet, and rice from FAOSTAT and the global yield gap atlas to assess the sensitivity both nationally and sub-nationally. Historical data on mean crop growing season and mean annul precipitation were collected from a collaborative database of UNDP/Oxford University and the climate portal of the World Bank to assess the exposure both nationally and sub-nationally. To assess adaptive capacity, literacy, and poverty rate proxies for both the national and regional scales were collected from KNOEMA and the African Development Bank. These data were analyzed using a vulnerability index that is based on sensitivity, exposure, and adaptive capacity. The national scale results show that millet has the lowest vulnerability index while rice has the highest. An inverse relationship between vulnerability and adaptive capacity is observed. Rice has the lowest adaptive capacity and the highest vulnerability index. Sub-nationally, this work has shown that northern maize is the most vulnerable crop followed by western highland rice. This work underscores the fact that at different scales, crops are differentially vulnerable due to variations in precipitation, temperature, soils, access to farm inputs, exposure to crop pest and variations in literacy and poverty rates. Therefore, caution should be taken when transitioning from one scale to another to avoid generalization. Despite these differences, in the sub-national scale, western highland rice is observed as the second most vulnerable crop, an observation similar to the national scale observation.

The Tensift basin in Morocco is prominent for its ecological and hydrological diversity. This is marked by rivers flowing into areas such as Ourika. In addition to agriculture, the basin is a hub of variable land use systems. As such, it is important to gain a better understanding of the relationship between simulated and observed precipitation in this region to be able to better understand the role of precipitation in impacting the climate and water resources in the basin. This study evaluates the performance of multi-source satellite products against weather station precipitation in the basin. The satellite-product-based data were first collected for seven satellite products, namely PERSIANN, PERSIANN CDR, TRMM3B42, ARC2, RFE2, CHIRPS, and ERA5 (simulated precipitation) from the following repositories (CHRS iRain, RainSphere, NASA, EUMETSAT, NOAA, FEWS NET, ECMWF). Precipitation observation data from six weather stations, located at Tachedert (2343 m), Imskerbour (1404 m), Asni (1170 m), Grawa (550 m), Agdal (489 m), and Agafay (487 m), at different altitudes, latitudes, and temporal scales (1D, 1M, 1Y), over the period 13 May 2007 and 31 September 2019 over the Tensift basin were collected. The data were compared and analyzed through inferential statistics such as the Nash–Sutcliffe efficiency coefficient, bias, root-mean-square error (RMSE), root-mean-square deviation (RMSD), the standard deviation, the correlation coefficient (R), and the coefficient of determination (R²) and visualized through Taylor diagrams and scatterplots to visualize the closeness between the seven satellite products and the observed precipitation data. A second analysis was carried out on the monthly precipitation, resulting from the six weather stations, and based on the standardized precipitation index (SPI) to determine the onset, duration, and magnitude of the meteorological drought. The results show that PERSIANN CDR performs best and is more reliable regarding its ability to simulate precipitation over the basin. This is seen as PERSIANN CDR has significant rates for the different statistics (Bias: −0.05 (Daily Asni), RMSE: 2.86 (Daily Agdal), R: 0.83, R²:0.687 (Monthly Agdal)). The results also show that there are no major differences between the observed weather station and the satellite precipitation data. The best performance was attributed to PERSIANN CDR (for monthly and annual precipitation at all altitudes and for daily precipitation at high altitudes). However, most of the time, this product records low or negative Nash values (−6.06 (Annual Grawa)), due to the insufficient weather station data in the study area (Tensift). It was observed that TRMM overestimates precipitation during heavy precipitation and underestimates it during low precipitation. This makes it important for the latter observations to be viewed with caution due to the quality of annual comparison results and underscores the need to develop more efficient precipitation comparison approaches and datasets. Additionally, the performance of the satellite products is better at low altitudes and during wet years. Finally, it was concluded from the SPI that Tensift region has experienced 13 drought periods over the study period, with the longest event of 12 months being from Marsh 2015 to February 2016, and the most intense event with the highest drought severity (19.6) and the lowest SPI value (−2.66) being in 2019.