Kemal Adem Abdela

River riparian basins play a crucial role in mitigating greenhouse gas (GHG) emissions through carbon sequestration and nitrogen sinks. However, increased ecological stresses led to the release of CO₂, CH₄ and N₂O. This study aimed to investigate how extreme temperatures, water levels, moisture content, land use changes and soil composition influence GHG emissions in the riparian corridor and to recommend mitigation techniques. It was carried out at the Yangtze River Riparian zone, China, using soil column testing. It used soil column testing. The results showed that extreme temperatures caused the highest emissions of CO₂ (29–45%), CH₄ (24–43%) and N₂O (27–33%). This was due to increased soil temperatures and accelerated organic carbon/nitrogen decomposition. Conversely, control and wet–dry cycles absorbed CO₂ (1–3%), CH₄ (3–10%) and N₂O (1–21%) by improving soil aeration, increased oxygen availability, soil structure, stable water table and low temperature change. Grasses in riparian areas also improved carbon sinks. Highest water levels had lowest gas concentrations and emissions due to low oxygen level. Adaptive wet‐dry cycles, grass cover and better water table management can restore riparian areas, maintain soil moisture, balance soil carbon/nitrogen levels and mitigate climate change by improving soil quality. Dissolved organic matter fluorescence (DOMFluor) components are essential for soil carbon dynamics, aquatic biome safety, nutrient cycling and ecological balance in riparian zones. The study recommends implementing restoration practices, managing soil moisture, afforestation, regulating temperature and monitoring water tables to mitigate GHG emissions and address climate change. Future policies should focus on promoting resilient land use and ecosystems.

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts.

This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning.

This bibliographic article on Drought and Water Level examined the relationship between organizations, nations, institutions, authors, references, and publishers. It examined 742 papers from Web of Science at the Nanjing University of Information Science and Technology’s. The total annual publication volume of articles was increased steadily from 2012 to 2021, with China and the United States ranking first and second in terms of publication volume and citations but in quality Switzerland and England were top-level. Institutional-partnership analyses indicated disparities in network density and connections, with the Chinese Academy of Sciences (2012) receiving the highest citations and degrees. The document co-citation analysis (DCA) network was created to improve understanding of the frequency and amplitude of bursts of various publications in separate clusters. The most cited work was J Hydrol (2012), with 302 citations. The analytical tool from CiteSpace collected high-frequency keywords and performed co-occurrence, grouping, and emerging word recognition. Gorges Dam is the most crowded cluster, followed by drought stress. The greatest burst duration and most significant phrase is reservoir (2019), followed by “water quality,” which has a 5 year burst period. Estuaries perform important functions such as water purification and coastal. “Reservoir, water quality, restoration, phytoplankton, temperature, wetland, time series, diversity and carbon dioxide” are the most important terms, while “climate change, drought, water level, impact, growth, variability, response, dynamics, management and model” are the most frequently used keywords. In terms of citations, references, and academic influence, Zhang Q. (2012), the R Core team (2014), and Jappen E. (2015) were the top three contributors. Cook, ER (2013), and Allen, R.G. (2019) ranked first and second in terms of frequency, respectively. In this review work, significant information gaps were discovered in the areas of microbiological dynamics, environmental variables, fen peat incubation, lake water, drought risk reduction, biological ecology, lake acidification, salinity variations, and attribution. Future researchers should focus on these and similar topics, while Chinese and USA authors should concentrate on article quality rather than publishing numbers.