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Climate change increases the frequency and intensity of extreme events such as droughts, heat waves, and floods, posing a significant challenge to Morocco’s agriculture and food security. Understanding the future impact of climate on crop yield is crucial for long-term agricultural planning. However, this area has been underexplored due to various challenges, including data constraints. This study aimed to project wheat yield in Morocco at a provincial scale from 2021 to 2040 by using multiple climate model datasets, and advanced Machine Learning (ML) algorithms. An ensemble of five global climate models (MIROC6, CanESM5, IPSL-CM6A-LR, INM-CM5-0, NESM3) was employed to project changes in temperature (Tmax, Tmin) and precipitation (Pr). The climate projections were bias corrected using quantile-quantile approach. Four advanced ML algorithms: Random Forest, XGBoost, LightGBM, and Gradient Boosting Regressor, were utilized to develop a stacked ensemble learning model for wheat yield prediction at provincial scale in Morocco. The stacked ensemble learning model was calibrated and validated using historical wheat yield data. Results show that the stacked ensemble learning approach significantly reduced prediction errors compared to individual models, achieving high coefficient of determination of 0.82 and low root mean square error (RMSE) of 300.51 kg/ha. Wheat yields are projected to decline by an average of 10% by 2040 under the modest shared socioeconomic pathways (SSP2-4.5) scenario while under high emission scenario (SSP5-8.5), yield could decrease by up to 60% across some provinces such as Essaouira, Youssoufia, Ouezzane, Rehamna, and Sidi Kacem. Temperature (Tmax and Tmin) and precipitation (Pr) were identified as the critical climate variables influencing wheat yield, with Tmax being the most impactful. Regional projections revealed that provinces inland and in southern Morocco may experience a significant yield reduction of up to 60%. This study highlights the need for implementing effective climate change mitigation measures to avert food insecurity in Morocco and other northern African countries. The primary findings indicate that climate variables, particularly Tmax, play a crucial role in wheat yield projections, emphasizing the importance of detailed climate data and advanced modeling techniques in agricultural planning.

AbstractThe study area, affected by war and siege, necessitates a study of its current ecological status. The objective of this study was to assess the present forest structure, species diversity, and Total Biomass Carbon Stock (TBCS) in relation to environmental variables. The findings shown 42 woody plant species, with 23 in the highland and 41 in the midland. No significant difference in species abundance is observed between the highland and midland areas. However, there are significant differences in the average diameter and height of trees between the altitudinal classes. Additionally, diversity indices shown significant difference, with the midland shown higher values than the highland. TBCS of the forest, the highland and midland areas were 21.02, 15.6, and 22.92 tons/ha, respectively. Moreover, TBCS shown significant contributions from tree diameter and height (p < 0.05) whereas species diversity, richness, and abundance do not shown a significant influence. This research further assesses factors influencing carbon stock, identifying annual mean temperature, soil organic content, temperature seasonality, and altitude as key contributors. Dominant species are limited, with around 50% classified as rare. Hence, our study suggests that the implementation of effective forest management practices aimed at enhancing biodiversity and ecosystem services.

Climate change significantly challenges the sustainability of forest ecosystems, with broad socio-ecological impacts insufficiently assessed. This study examines one such critical system, the Globally Important Agricultural Heritage Systems (GIAHS), focusing on preserving the Argan tree in the Ait Souab-Ait Mansour region of southern Morocco. The region, including the Massa and Tamanart river basins, is crucial for Argan tree conservation, yet there is limited information on how climate change will affect the tree. This study aims to analyze climate variability trends and potential impacts on Argan tree distribution using observed (1983–2022) and projected rainfall and temperature data up to 2080. The MaxEnt model projected Argan tree distribution for 2041–2060 and 2061–2080 under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Findings reveal significant climatic disparities, with warming of 0.4 °C between 1983 and 2022 and future temperatures reaching up to 4 °C between 2041 and 2080. Rainfall shows considerable variability, with a decrease of approximately 2.5 mm per decade during the rainy season. Projections indicate a decline in annual precipitation, impacting seasonality and increasing the frequency of extreme events during the argan fruit production season. Suitable areas for Argan trees are expected to decline, particularly in the Tamanart and Massa River basins. The study highlights a 47–65% reduction in highly adapted zones in the med term (2041–2060), and 49–81% in the long term (2061–2080), emphasizing the need for proactive adaptation measures in effort to conserve and increase Argan tree’s survival and productivity.

AbstractThermal discomfort and heat stress have become common concerns, especially in an urbanized environment. This work investigates the changes in thermal comfort of Fiji’s urban centers. The analysis is based on Thom’s discomfort index (DI), calculated from observed temperature and humidity data during 1985–2019. Discomfort is highest in austral summer (November – April) and lowest in winter (June – August). Discomfort increased from the late 1990s to 2000s. Given that the urban centers under this study are still expanding, the level of discomfort is expected to worsen. The observed thermal comfort condition is likely to have an adverse impact on the urban population, especially the elderly people. In addition, a higher DI is likely to affect the country’s tourism industry. Proper building designs and increase in the country’s vegetation covers are practical and effective measures to manage the discomfort. There remains a need for instantaneous change in DI studies, monitoring its changes using high-resolution datasets, and to assessing the people’s actual thermal feeling based on a survey.

•Key predictors influencing species distribution were soil, climate, and elevation.•Species suitability differences between current and future periods were observed.•Species distribution were predicted to shift to higher altitudes in the future.•Increasing the species suitable area leads to rise in their carbon stock.

AbstractEthiopia’s forest fires are causing biodiversity loss and ecosystem destruction, highlighting the need for preventative measures. However, there is limited knowledge regarding the future impact of climate on forest fires in Ethiopia. Hence, this study primarily examines the past and future climate patterns and their potential impact on occurrence of wildfires. Climate data from the baseline (1980–2020) were projected for the future period (2021–2099) under RCP4.5 and RCP8.5 scenarios using R-software. The trends in climate were analysed using Anclim and Weather-Datatool software. To estimate the occurrence of fire, Keetch-Byram-Drought-Index was calculated using climate data and antecedent soil moisture. The result of the study indicates a projected increase in temperature, with variation in precipitation. A moderate fire risk is projected in 2021–2039 that increases to moderate-to-high probability in 2040–2069 and 2070–2099. In the future, the annual severity of fires will be 27.2–27.8% moderate and 3.3–7.8% high. The highest risk of fire will be experienced in the dry season, more in the 2070–2099 under RCP8.5 scenario. Based on the analysis this study concludes that climate change is expected to have an impact on the occurrence of wildfires. The study emphasizes the importance of taking proactive measures for effective fire management.

The decline in March–May (MAM) ‘boreal spring’ rainfall is of great concern to Kenya’s agricultural sector. This study examines factors influencing MAM rainfall variability based on monthly observed and Climatic Research Unit (CRU) reanalysis rainfall datasets for the period 1971–2010. The distribution patterns of MAM rainfall were analyzed using Empirical Orthogonal Functions (EOF), whereas the Sequential Mann–Kendall (MK) statistic was used for trend analysis. Factors influencing seasonal rainfall were determined and assessment of the circulation anomalies associated with wet/dry condition during the study period was carried out. The MAM rainfall revealed decreasing trend. Wet years are associated with enhanced low-level convergence and upper-level divergence of winds, advection of moisture flux following a well-positioned and intensified Arabian high-pressure cell, and an accompanying rising branch of Walker circulation over Indian Ocean (IO). The sea surface temperatures anomalies (SSTAs) in the central and sub-tropical IO are closely related to MAM rainfall over Kenya. Positive SSTAs accompanying negative OLR anomalies may lead to intensified rising branch of the Walker circulation influencing the MAM wet events in Kenya. There is a negative correlation coefficient of − 0.62 between MAM rainfall and Outgoing Long-wave Radiation (OLR) indicating that the inter-annual variation of the MAM rainfall and OLR are in opposing phases and, hence, more convection and enhanced rainfall over the study domain. The study reveals that the circulation anomalies associated with the dry years are opposite to those in wet years, forming a good basis of monitoring similar events in future.

The demand for power in Kenya is on the increase with the ongoing growth of the country’s economy. There is a need for the country to balance energy efficiency, sustainability and low-carbon technologies. This entails drafting and implementing policies and strategies towards a low-carbon development path, ranging from fuels, technologies and infrastructure. This work examines the drivers of renewable energy resources in Kenya, focusing on Ngong Wind Farm. Results show that most low-carbon innovations in Kenya are driven by government tariffs and policies. Funding, and political and community goodwill remarkably influence the success of wind power projects in Kenya. The case study is a novel experiment that offers sustainable alternatives in the energy sector. There is need for more investment in the renewable sector, especially in the set up of power plants and power storage. To address the shortcomings in the renewable energy sector, there is a need for further research and development, and collaborations to foster innovations in the wind power sector in country. A combination of knowledge and resources, and leveraging local and national policies are potential ways in which institutional platforms can foster wind technology advancement and dissemination.

Land–atmosphere coupling is a key process of the climate system, and various coupling mechanisms have been proposed before based on observational and numerical analyses. The impact of soil moisture (SM) on evapotranspiration (ET) and further surface temperature (ST) is an important aspect of such coupling. Using ERA-Interim data and CLM4.0 offline simulation results, this study further explores the relationships between SM/ST and ET to better understand the complex nature of the land–atmosphere coupling (i.e., spatial and seasonal variations) in eastern China, a typical monsoon area. It is found that two diagnostics of land–atmosphere coupling (i.e., SM–ET correlation and ST–ET correlation) are highly dependent on the climatology of SM and ST. By combining the SM–ET and ST–ET relationships, two “hot spots” of land–atmosphere coupling over eastern China are identified: Southwest China and North China. In Southwest China, ST is relatively high throughout the year, but SM is lowest in spring, resulting in a strong coupling in spring. However, in North China, SM is relatively low throughout the year, but ST is highest in summer, which leads to the strongest coupling in summer. Our results emphasize the dependence of land–atmosphere coupling on the seasonal evolution of climatic conditions and have implications for future studies related to land surface feedbacks. 陆-气耦合是气候系统中的重要过程, 已经有大量基于观测和数值模拟的研究提出了各种耦合机制. 土壤湿度影响蒸散发进而引起地表温度异常是陆-气耦合研究中的重要组成部分. 利用ERA-Interim再分析资料和CLM4.0模拟结果, 本研究进一步探讨了土壤湿度/地表温度与蒸散发之间的关系, 以更好地理解中国东部地区陆-气耦合的复杂性质(即空间和季节变化). 本研究发现陆-气耦合的两个诊断量(即土壤湿度与蒸散发的相关系数和地表温度与蒸散发的相关系数)的变化主要依赖土壤湿度和地表温度的气候状态, 存在明显的空间变化和季节演变. 结合两个相关系数, 本研究确定了中国东部的两个陆-气耦合的关键区: 西南和华北地区. 在西南地区, 土壤湿润, 温度较高, 但在旱季的时候土壤湿度显著下降, 春季达到最低, 因此春季表现为较强的陆气耦合. 而在华北地区, 土壤湿度在年内维持在较低的水平, 仅在较为温暖的季节才有足够的能量将土壤中的水分蒸发至大气, 因此陆-气耦合强度随着温度的季节变化而发生改变, 夏季最强. 本文的研究结果强调了陆-气耦合对气候条件季节演变的依赖性, 为未来有关陆面过程反馈的研究提供一定的参考.