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This study aims to investigate aspects related to the changing trends of the rainfall extremes in the entire Mekong Delta in the period of 32 years (1984-2015) applying rainfall extreme indices. First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen’s slope estimate.

First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen’s slope estimate.

The results of R20 pointed out that an insignificant upward tendency was found in the coastal provinces, whereas an insignificant downward tendency was also recorded in the inland provinces. Regarding the number of R50, a similar trend to R20 was recorded with five stations slightly increased and five stations slightly decreased. For the number of R100, the results recorded an absence of significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend.

For the number of R100, the results recorded an absence of the significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend. Of note is the fact that the number of R100 occurred more frequently in the northern provinces, which means the northern region is facing a high risk of flooding.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

Change of climate is attributed to human activity that alters the composition of the global atmosphere observed over comparable periods. The purpose of this paper is to explore smallholder farmers' perceptions of climate change and compare it with meteorological data, as well as to identify perceived adaptation barriers and examine the factors that influence the choice of adaptation options in eastern Ethiopia.

In total, 384 sample households were chosen from four districts of the zone. A cross-sectional survey was used to conduct the study. Primary data was acquired through key informant interviews, focus group discussions and semistructured interviews, whereas meteorological data was collected from the National Meteorological Service Agency of Ethiopia. A Mann–Kendall statistical test was used to analyze temperature and rainfall trends over 33 years. A multivariate probit (MVP) model was used to identify the determinants of farmers' choice of climate change adaptation strategies.

The result indicated that temperature was significantly increased, whereas rainfall was significantly reduced over the time span of 33 years. This change in climate over time was consistently perceived by farmers. Smallholder farmers use improved varieties of crops, crop diversification, adjusting planting dates, soil and water conservation practices, reducing livestock holdings, planting trees and small-scale irrigation adaptation strategies. Moreover, this study indicated that sex of the household head, landholding size, livestock ownership, access to extension, access to credit, social capital, market distance, access to climate change-related training, nonfarm income, agroecological setting and poverty status of the households significantly influence farmers’ choice of adaptation strategies.

Further research is required to evaluate the economic impact of each adaptation options on the livelihood of smallholder farmers.

Institutional variables significantly influenced how farmers adapted to climate change, and all of these issues might potentially be addressed by improving institutional service delivery. To improve farm-level adaptation, local authorities are recommended to investigate the institutional service provision system while also taking demographic and agroecological factors in to account.

This study compared farmers' perceptions with temperature and rainfall trend analysis, which has been rarely addressed by other studies. This study adopts an MVP model and indicated the adaptation strategies that complement/substitute strategies each other. Furthermore, this study discovered that the choice of adaptation options differed between poor and nonpoor households, which has been overlooked in previous climate change adaptation research.

This study aims to explore the smallholder farmers’ perceptions of climate change and its adaptation options (changing crop variety; improved crop and livestock; soil and water conservation [SWC]; and irrigation practices) and drought indices in the Dire Dawa Administration Zone, Eastern Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households for key informants and focus group discussions were used. This study used both descriptive statistics and an econometric model. The model was used to compute the determinants of climate adaptation options in the study area. Drought characterization was carried out by DrinC software.

The results revealed households adapted to selected adaptation options. The model results confirmed that education level, farm size, tropical livestock units (TLUs) and access to agricultural extension services have positive and significant impacts on changing crop variety by 0.0014%, 0.045%, 0.032% and 0.035%, respectively. The likelihood of farmers’ decisions to use adaptation strategies (family size, TLU, agricultural extension service and distance from the market) has positive and significant impacts on SWC. The reconnaissance drought index (RDI6) of ONDJFM and AMJJAS showed extreme and severe drought index values of −2.88 and −1.96, respectively.

This study used a locally adopted climate change adaptation intervention for smallholder farmers, revealing the importance of drought characterization indices both seasonally and annually.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

Climate change and variability present different challenges to the livelihoods of forest-dependent communities. This paper aims to determine climate variability/change and its effects on the livelihoods of the Buyangu community, which depends on Kakamega tropical rain forest in Kenya.

Rainfall and temperature trends were analysed using Mann–Kendall tests and Sen’s slope estimator. The effects of climate variability on the community were determined using household survey questionnaires, focus group discussions and in-depth interviews with key stakeholders.

Temperature trend analyses represent statistically significant trends for the period of 1980-2015. Results reveal a warming trend for both mean annual maximum temperatures and mean annual minimum temperatures by 0.04°C/year and 0.02°C/year, respectively. Moreover, analysis of annual precipitation (1923-2015) indicated an increase of 0.068 mm/year; however, the mean monthly rainfall showed a decreasing trend. As a result, crop production and livestock rearing are negatively affected. Although there is a high level of awareness of climate variability and its related effects on livelihoods, a majority of the Buyangu community still do not understand the influence of climate change on forests and the provision of forest products. Lack of knowledge on this subject will consequently limit adaptation responses.

This research fulfills the need to study climate variability and its effects on the livelihoods of forest-dependent communities. The study calls for all-round stakeholder participation of local and national players in formulating coherent adaptation strategies that will enhance the resilience of forest-dependent communities to a changing climate.

Open access journals (OAJs) offer immediate, free and unrestricted online access to the scholarly literature. The purpose of this study is to trace the status and characteristics of OAJs published across the globe. Various trends that have evolved in OAJ market have been studied.

The study is based on data collected from one of the largest OA journal directory – Directory of Open Access Journals (DOAJ). The data were downloaded on 02 January 2018 and details of OAJs added to DOAJ till 31 December 2018 were harvested, codified and further analyzed in SPSS software. A Microsoft-Excel template application – MAKESENS – developed by Finnish Meteorological Institute (Finland) in 2002, was explored to perform Mann–Kendall Test and Sen’s Slope Estimates.

A less score of OAJs offer access to their archival content. An increasing trend is witnessed in the OAJ publishing with Elsevier, Sciendo and BioMed Central (BMC) as the top publishers. Majority of publishers are from high-income zone countries, followed by upper-middle and lower-middle zone countries. Majority of OA publishers are from the UK, Indonesia and Brazil. A lesser score of journals offer article processing charges and/or author submission charges. Majority of OAJs from high- and lower-middle-income zone countries levy submission/processing charge to authors compared to OA journals from upper-middle- and lower-income zone countries (p < 0.01). OJS stays a prioritized platform for hosting OA journal content. Portico and CLOCKSS/LOCKSS are mostly used for long-term preservation purposes. Majority of OAJs from high-income zone countries participate in digital arching initiatives compared to ones from other income zones. Majority of the journals adopt a peer review (double-blind peer review, blind peer review, peer review and open peer review) process for validation of their scholarly content. The time lag between submission and publication ranges from one to 53 weeks, with majority of OAJs having a time lag of 11-20 weeks. Creative Commons Licenses are mainly adopted by OAJs.

As the study is based on the data offered by DOAJ, any gaps in the DOAJ data will also get reflected in the study. Further, there might be other OAJs also that have yet to show compliance with DOAJ standards and get indexed with it.

The study tries to showcase the current status and characteristics of OAJs.

This paper generally aimed to evaluate the knowledge of rural farmers towards climate change (CC) and their adaptation practices as a way of reducing the vulnerability in different agro-ecological zones (AEZs) of Tanzania. This establishment is quite important because rural farmers are most vulnerable to impacts of CC. Therefore, there is a need to establish the actual knowledge on this aspect in order to meet the urgency for action on CC by using the endowed environmental resources. This knowledge will assist policy makers and CC practitioners.

This study employed systematic and simple random sampling procedures in selecting the districts and villages as study sites. It used both quantitative and qualitative data sets derived from climatic records, questionnaires survey, interviews and discussion. The Mann-Kendall test and Sen's slope, and theme content were used for data analyses.

The results exhibited that most farmers have been greatly adapting to CC through numerous and well-known practices. However, there has been adoption of other new practices mainly influenced by new knowledge from extension services, environmental endowments and temporal environmental change. Although there has been varied perceptions on CC, the results indicate that most farmers (>70%) are aware of the changing climate and their knowledge corresponds to meteorological data.

Most farmers perceived that prolonged drought and erratic rains are the main indicators of CC. These indicators were the base of copping and adaptation mechanisms. Despite this, there is a need for all climate practitioners to join hands in improving the existing adaptation practices that can limit climate impacts in local areas.

This study aims to quantify sectoral energy and carbon intensity, revisit the validity of the Environmental Kuznets Curve (EKC) and explore the relationship between economic diversification and CO₂ emissions in Bahrain.

Three stages were followed to understand the linkages between sectoral economic growth, energy consumption and CO₂ emissions in Bahrain. Sectoral energy and carbon intensity were calculated, time series data trends were analyzed and two econometric models were built and analyzed using the autoregressive distributed lag method and time series data for the period 1980–2019.

The results of the analysis suggest that energy and carbon intensity in Bahrain’s industrial sector is higher than those of its services and agricultural sectors. The EKC was found to be invalid for Bahrain, where economic growth is still coupled with CO₂ emissions. Whereas CO₂ emissions have increased with growth in the manufacturing, and real estate subsectors, the emissions have decreased with growth in the hospitability, transportation and communications subsectors. These results indicate that economic diversification, specifically of the services sector, is aligned with Bahrain’s carbon neutrality target. However, less energy-intensive industries, such as recycling-based industries, are needed to counter the environmental impacts of economic growth.

The impacts of economic diversification on energy consumption and CO₂ emissions in the Gulf Cooperation Council petroleum countries have rarely been explored. Findings from this study contribute to informing economic and environment-related policymaking in Bahrain.

Previous research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.

First, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.

The new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.

This study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.