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This study deals with the statistical analysis of climate variables (temperature, relative humidity, and rainfall) and the analysis of data and values of climate characteristics for the period of 2006- 2016 in Aden Governorate. The normal distribution of climate variable data was verified, and the values of basic descriptive statistics were determined. The differences between their means were identified, the relationship between climate variables and rainfall was studied, and the effect of both temperature and relative humidity on the rainfall was considered. The data for the climate variables under study was found to be normally distributed, and there were no statistically significant differences between the values of the maximum and minimum temperature variables. It was found that there were statistically significant differences between the values of the relative humidity variable and the rainfall variable, as well as a strong statistically significant direct relationship between the maximum and minimum temperatures, and a strong statistically significant inverse relationship between maximum temperatures and relative humidity, as well as a moderate statistically significant inverse relationship between minimum temperatures and relative humidity, while there was a weak direct relationship between temperatures (Maximum and minimum) and the rainfall. Furthermore, there was a weak inverse relationship between relative humidity and rainfall, and this is a reflection of the location of Aden Governorate on the southern coastal plains of Yemen, which is located within the hot tropical region. There was no statistically significant effect of climate variables (maximum and minimum temperatures, and relative humidity) on the rainfall in Aden Governorate, due to its location and the influence of other factors such as the direction of the winds blowing onto it, especially in the summer. [ABSTRACT FROM AUTHOR]

This study investigated the long-run and short-run relationships between solid waste generation in Nigeria and two key climate variables: rainfall and temperature. Employing a Vector Error Correction Model (VECM) analysis on data from 1982 to 2022, then revealed counterintuitive findings. In the long run, lagged rainfall exhibits a negative association with solid waste (p < 0.05), potentially explained by increased waste decomposition in wetter conditions. Conversely, lagged temperature showed a positive association (p < 0.05), aligning with theories of increased consumption and economic activity in warmer periods. The shortrun analysis unveils a self-correcting mechanism in solid waste generation and a statistically significant negative impact of lagged temperature (p < 0.05), requiring further investigation. Based on these findings, the study proposed policy implications for waste management strategies and data collection, emphasizing the need for sustainable solutions in the context of climate change. [ABSTRACT FROM AUTHOR]

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks. [ABSTRACT FROM AUTHOR]

Spatial Autoregressive (SAR) models are used to model the relationship between variables within a specific region or location, considering the influence of neighboring variables, and have received considerable attention in recent years. However, when the impact of exogenous variables becomes notably pronounced, an alternative approach is warranted. Spatial Expansion, coupled with the Casetti model approach, serves as an extension of the SAR model, accommodating the influence of these exogenous variables. This modeling technique finds application in the realm of rainfall prediction, where exogenous factors, such as air temperature, humidity, solar irradiation, wind speed, and surface pressure, play pivotal roles. Consequently, this research aimed to combine the SAR and Spatial Expansion models through the Casetti model approach, leading to the creation of the Spatial Autoregressive Exogenous (SAR-X) model. The SAR-X was employed to forecast the rainfall patterns in the West Java region, utilizing data obtained from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) dataset. The practical execution of this research capitalized on the computational capabilities of the RStudio software version 2022.12.0. Within the framework of this investigation, a comprehensive and integrated RStudio script, seamlessly incorporated into the RShiny web application, was developed so that it is easy to use. [ABSTRACT FROM AUTHOR]

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10latitude with Maharashtra's monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model's performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2. [ABSTRACT FROM AUTHOR]

Dengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand drivers of dengue transmission. Such tools are critical for providing timely information to assist healthcare authorities in preparing human, material, and medical resources for outbreaks. Here, we investigate associations between meteorological variables and dengue transmission in the Dominican Republic in 2019, the year in which the country's largest outbreak to date ocurred. We apply generalized linear mixed modelling with gamma family and log link to model the weekly dengue incidence rate. Because correlations in lags between climate variables and dengue cases exhibited different behaviour among provinces, a backward-type selection method was executed to find a final model with lags in the explanatory variables. We find that in the best models, meteorological conditions such as temperature and rainfall have an impact with a delay of 2–5 weeks in the development of an outbreak, ensuring breeding conditions for mosquitoes. [ABSTRACT FROM AUTHOR]

Several studies have highlighted the significant impact of climate change on agriculture. However, there have been little empirical enquiries into the impact of climate change on marine fish production, particularly in Bangladesh. Hence, this study aims to investigate the impact of climate change on marine fish production in Bangladesh using data from 1961 to 2019. Data were obtained from the Food and Agriculture Organization, Bangladesh Meteorological Department, the World Development Indicators, and the National Oceanic and Atmospheric Administration. The autoregressive distributed lag (ARDL) model was used to describe the dynamic link between CO2 emissions, average temperature, Sea Surface Temperature (SST), rainfall, sunshine, wind and marine fish production. The ARDL approach to cointegration revealed that SST (β = 0.258), rainfall (β =0.297), and sunshine (β =0.663) significantly influence marine fish production at 1% and 10% levels in the short run and at 1% level in the long run. The results also found that average temperature has a significant negative impact on fish production in both short and long runs. On the other hand, CO2 emissions have a negative impact on marine fish production in the short run. Specifically, for every 1% rise in CO2 emissions, marine fish production will decline by 0.11%. The findings of this study suggest that policymakers formulate better policy frameworks for climate change adaptation and sustainable management of marine fisheries at the national level. Research and development in Bangladesh's fisheries sector should also focus on marine fish species that can resist high sea surface temperatures, CO2 emissions, and average temperatures. [ABSTRACT FROM AUTHOR]

Determining the actual evapotranspiration value and analyzing its temporal trend is essential for optimal water resources management in a basin. In the present paper, the actual evapotranspiration time series is simulated and its trend is analyzed according to the trend of climatic variables and land use in the Ajichi basin during the period of 2015- 1987. The comprehensive SWAT model was set up, calibrated, and validated for the Ajichi basin. Also, the average of simulated actual evapotranspiration of crops (in wet years) was compared with similar values in the National Water Document. The results of the Mann-Kendall trend test showed that the annual rainfall in most meteorological stations had a decreasing trend and the rainfall trend in the ten stations decreased significantly. While the annual maximum temperature at all stations and the annual minimum temperature in most of them have significantly increased. Investigation of land use maps illustrated that the irrigated land area of the basin has increased by a 39% during the study period. According the study's results, the potential evapotranspiration of the basin has had a significant increasing trend with a rate of 2.54 mm per year. The results indicated that despite the increasing trend of potential evapotranspiration and irrigated land area, the actual evapotranspiration of the basin had a significant decreasing trend with a rate of 2.2 mm per year due to the decrease in rainfall. [ABSTRACT FROM AUTHOR]

Climatic parameters are part of the main determinants of agricultural production in many developing countries including Nigeria. This study analyzes the vulnerability of vegetable crops to the impact of rainfall and temperature variability and the management techniques in Kwara State agro-ecological region of Nigeria. Data on rainfall, temperature and vegetables (tomato, pepper, okra, amaranthus and garden egg) were collected for a period of twenty-five (25) years from the four agricultural zones of Kwara State. These were subjected to descriptive, correlation and regression analyses. The result revealed that climatic variables examined fluctuate but there has been constant increase in temperature over the years and year 2001 recorded the highest (40.06oC). Vegetable crop yield also fluctuates. Correlation analysis revealed that tomato correlates positively with rainfall, maximum and minimum temperature. Okra and pepper correlate positively with rainfall and maximum temperature and inverse correlation with minimum temperature. Amaranthus has an inverse correlation with rainfall and maximum temperature but correlates positively with minimum temperature although very low. Garden egg correlates with rainfall and inversely correlates with maximum and minimum temperature. Hence, vegetable crops examined are vulnerable to the impact of climatic variables but not too strong as there are other factors such as the nature of soil, specie of seedlings, chemicals among others that contributed to vegetable crop yield. Management techniques suggested to improve the productivity of vegetable crops in Kwara State include the use of modern agricultural techniques such as development of irrigation and water harvesting technologies.

Trend analysis of streamflow and its linkages to rainfall can facilitate the development of water resource management plans for watersheds. The present study focuses on trends in observed streamflow of the lower Tamiraparani river sub-basin in Tamilnadu, India using Mann - Kendall test and Sen's slope estimator for a period of 30 years. From this analysis, both the annual streamflow, annual rainfall increased very marginally at the rate of 0.1799 m3/s/year and 4.3741 mm/year, respectively. Similarly, in the northeast monsoon period, both the rainfall and streamflow were increased very minimally during the analysis period. However, the observations made during the southwest monsoon showed declining trends for most of the rainfall stations and no significant trend was observed for streamflow. The calculated Spearman's Rho correlation factor (p) between rainfall and streamflow showed weak correlation during summer (0.26) and very strong correlation during northeast monsoon (0.83), strong correlation during southwest monsoon (0.53) and moderate correlation during winter (0.40). From this analysis, using Mann - Kendal test and Spearman's correlation it implies that other than precipitation and temperature, urbanization, watershed and ecological development of the catchment area could have also influenced the trends of streamflow. [ABSTRACT FROM AUTHOR]

In this study, we quantify the link between climate variables, such as temperature, rainfall, humidity, number of tropical cyclones, and number of thunderstorms, and seasonal variation in the Hong Kong inbound tourism demand from mainland China, Taiwan, South Korea, and Japan using the average Euclidean distance (AED) statistics and tourism demand modeling approach. Seasonal variation is measured as the fluctuation in the number of tourists from one season to another. Based on the smallest AED value, results of this study showed that climate variables play a dominant role in shaping seasonal variation relative to season and country. The fluctuation of tourist numbers from one season to another is determined not only by climate variables (e.g., the temperature of Hong Kong vs. the temperature in the country of origin of the tourist) but also by economic factors, including price and income. The variation in the impact of relative temperature depends on the country of origin. [ABSTRACT FROM AUTHOR]

Warming of the Earth climate system is unequivocal. That climate changes affect the stability of natural and engineered slopes and have consequences on landslides, is also undisputable. Less clear is the type, extent, magnitude and direction of the changes in the stability conditions, and on the location, abundance, activity and frequency of landslides in response to the projected climate changes. Climate and landslides act at only partially overlapping spatial and temporal scales, complicating the evaluation of the climate impacts on landslides. We review the literature on landslide-climate studies, and find a bias in their geographical distribution, with large parts of the world not investigated. We recommend to fill the gap with new studies in Asia, South America, and Africa. We examine advantages and limits of the approaches adopted to evaluate the effects of climate variations on landslides, including prospective modelling and retrospective methods that use landslide and climate records. We consider changes in temperature, precipitation, wind and weather systems, and their direct and indirect effects on the stability of single slopes, and we use a probabilistic landslide hazard model to appraise regional landslide changes. Our review indicates that the modelling results of landslide-climate studies depend more on the emission scenarios, the Global Circulation Models, and the methods to downscale the climate variables, than on the description of the variables controlling slope processes. We advocate for constructing ensembles of projections based on a range of emissions scenarios, and to use carefully results from worst-case scenarios that may over/under-estimate landslide hazards and risk. We further advocate that uncertainties in the landslide projections must be quantified and communicated to decision makers and the public. We perform a preliminary global assessment of the future landslide impact, and we present a global map of the projected impact of climate change on landslide activity and abundance. Where global warming is expected to increase the frequency and intensity of severe rainfall events, a primary trigger of rapid-moving landslides that cause many landslide fatalities, we predict an increase in the number of people exposed to landslide risk. Finally, we give recommendations for landslide adaptation and risk reduction strategies in the framework of a warming climate. [ABSTRACT FROM AUTHOR]

Aydin is the most important province of Turkey in terms of number of olive trees, olive and olive oil production. Generally, olive cultivation is made as traditional in Aydin. Not being applied enough of cultural processes in traditional olive cultivation causes production to be connected directly to climate variables. In the study, the yield and climate data covering the years 1990/2007 and total of five districts including the central district in Aydin were used. In this study, correlation coefficient between olive production and climate data of province and districts in Aydin, the needs for total of tempature requirement and cold days were calculated. According to the findings, in Aydin province, olive trees are in need of average of 2392.08 day-degree temperature from the rest of to harvest the fruit until the last time out was determined Growing Degree-Day Requirement . For Aydin province to meet the need to restore the olive tree is expressed as the sum of the temperature of the Chilling Requirement was calculated as 817.8 hours. In this model, average humidity and average sun hours for Aydin central district, only average sunny days for Kusadasi, soil temperature, average humidity and average wind and total rainfall for Nazilli, average maximum temperature and average wind for Sultanhisar was seen statistically important. [ABSTRACT FROM AUTHOR]

Nadal et al. (2018) recently reported on changes in the phenology of the Common quail ( Coturnix coturnix ) in seven cloudy regions of Spain in relation to climate change. The authors used a long-term ringing database (1961–2014) and calculated the mean date for three biological stages: arrival at the breeding areas, stay and autumn departure. They observed that some of these phenological variables were associated with the climate variables of temperature and rainfall (Figs. 4 and 6 of their article). They also analysed the yearly variation in temperature and rainfall over the last 86 years, reporting an increase in temperature and a decrease in rainfall (Figs. 3 and 5 of their article). Based on these results, the authors suggested that the Common quail phenology has varied as a response to climate change in Spain, thus concluding that “ quail movements and breeding attempts are eco-synchronized sequentially in cloudy regions. Our results suggest that quails attempt to overcome the negative impacts of climate change and agricultural intensification by searching for alternative high-quality habitats ”. We disagree with two methodological aspects of the article by Nadal et al. (2018): (1) the estimation of the mean date of arrival, stay and departure in the different regions studied; and (2) the analyses carried out to correlate the phenology of the species with the changes in climate variables. [ABSTRACT FROM AUTHOR]