Projections of future bioclimatic indicators using bias-corrected CMIP6 models: a case study in a tropical monsoon region.

This study evaluates the potential impacts of climate change on Bangladesh by analyzing 19 bioclimatic indicators based on temperature and precipitation. Data from 18 bias-corrected CMIP6 global climate models (GCMs) were used, covering four Shared Socioeconomic Pathways (SSPs)-SSP126, SSP245, SSP370, and SSP585-across three future timeframes: near-term (2015-2044), mid-term (2045-2074), and long-term (2075-2100). Under the high-emission SSP585 scenario, average temperatures are projected to rise by up to 3.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs).

Heatwaves in Peninsular Malaysia: a spatiotemporal analysis.

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future.

Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms.

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria.

Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration.

Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.

Seasonal correlation of meteorological parameters and PM with the COVID-19 confirmed cases and deaths in Baghdad, Iraq.

The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021.

Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs.

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters.

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM) concentration, the most harmful to human health, from meteorological and soil data.

Trace element speciation in sludge: a preliminary study to assess contamination levels in the sewage network.

The spreading of sewage sludge from wastewater treatment plants and various industries arouses the growing interest due to the contamination by trace elements. Sludges were collected from one sewage treatment plant and two industries in Dhaka City, Bangladesh to assess physicochemical parameters and total and fraction content of trace elements like Cr, Ni, Cu, As, Cd, Pb, Fe, Mn and Zn in sludges. We evaluated the bioavailability of theses metals by determining their speciation by sequential extraction, each metal being distributed among five fractions: exchangeable fraction, bound to carbonate fraction, Fe-Mn oxide bound fraction, organic matter bound fraction and residual fractions.

Projection of temperature extremes of Egypt using CMIP6 GCMs under multiple shared socioeconomic pathways.

Global warming has amplified the frequency of temperature extremes, especially in hot dry countries, which could have serious consequences for the natural and built environments. Egypt is one of the hot desert climate regions that are more susceptible to climate change and associated hazards. This study attempted to project the changes in temperature extremes for three Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.

Sources and management of marine litter pollution along the Bay of Bengal coast of Bangladesh.

Marine debris is often detected everywhere in the oceans after it enters the marine ecosystems from various sources. Marine litter pollution is a major threat to the marine ecosystem in Bangladesh. A preliminary study was conducted to identify the sources of marine litter (plastics, foamed plastic, clothes, glass, ceramic, metals, paper, and cardboard) along the Bay of Bengal coast.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria.

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors.

Farasan Island of Saudi Arabia confronts the measurable impacts of global warming in 45 years.

Coastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline.

Developing a high-resolution gridded rainfall product for Bangladesh during 1901-2018.

A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901-2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product's ability to capture spatial and temporal variability.

Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6.

Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) - home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2-4.

Vulnerability of Australia to heatwaves: A systematic review on influencing factors, impacts, and mitigation options.

Background: Heatwaves have received major attention globally due to their detrimental effects on human health and the environment. The frequency, duration, and severity of heatwaves have increased recently due to changes in climatic conditions, anthropogenic forcing, and rapid urbanization. Australia is highly vulnerable to this hazard.

Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios.

This study compared the performance of Long Short-Term Memory networks (LSTM) and Soil Water Assessment Tool (SWAT) in simulating observed runoff and projecting future runoff using 11 CMIP6 GCMs. The projected runoff was estimated for two Shared Socioeconomic Pathways (SSPs), 2-4.5 and 5-8.

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST), 15-cm (ST), and 30-cm (ST) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e.

Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data.

In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea.

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985-2014). The changes and uncertainty associated with the changes in PET in the near (2031-2060) and far (2071-2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2-4.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Unlabelled: Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis.

A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms.

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values.

Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran.

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model.