Search Results (172)

Climate classifications supply climate visualization with inference about general vegetation types. The Köppen classification system of thermal classes and an arid class is widely used, but options are available to strengthen climate change detection. For this study, I incorporated temperature and aridity information into all climate classes to isolate climate change, added a hypertropical class to better detect warming and drying in tropical zones, and developed a consistent ruleset of thermal classes with one temperature variable for streamlined application, yet maintained primary Köppen thermal classes. I compared climate currently to 6000 years ago (ka; Mid-Holocene) and 22 ka (Last Glacial Maximum) worldwide. Growing degree days > 0 °C was the most efficient variable for modeling thermal classes. Climate classes based on growing degree days matched 86% of Köppen thermal classes. Current climate shared 80% and 23% of class assignments with the Mid-Holocene and Last Glacial Maximum, respectively, with dry conditions shifting to the tropical and hypertropical classes under current climate. Contributing to our understanding of global environmental change, this classification demonstrated that the hypertropical class experienced the greatest change in area since 6 ka and the second greatest change in area since 22 ka, and the greatest increase in percentage arid classes during both intervals. The added hypertropical class with aridity information delivered sensitive detection of warming and drying for relevant climate classes under climate change. Full article

Consumption of fish containing elevated levels of radionuclides can lead to undesirable health effects for consumers. People in the Singida Municipality harvest fish from lakes and ponds of granite rocks which are linked with hazardous radioisotopes that may be bio-concentrated by fishes they consume. Currently, no study has ascertained the levels of radioisotopes in fish from these environments. This study was carried out to analyse the radioactivity levels of ²²⁶Ra, ²²⁸Ra and ⁴⁰K isotopes in order to assess the radiological risk associated with Tilapia fish consumption and its environment in Singida Municipality. Some 51 samples, which included water (20), sediment (20), Nile tilapia (8) and Manyara tilapia (3), were randomly sampled and composited; then, they were analysed using a high-purity germanium (HPGe) detector, between May and June 2022. The results revealed that (i) the activity levels of ²²⁸Ra were below the detection limit for fish and water samples, while in sediment, the combined activity of ²²⁸Ra was within the acceptable international levels; (ii) the mean activity concentrations of ²²⁶Ra and ⁴⁰K in all other samples were within the recommended levels; (iii) the activities of radionuclides in the samples analysed were high in sediments, followed by fish, and lastly water; (iv) the bioaccumulation results show that only ⁴⁰K was bio-accumulated (with 1.26 in Nile tilapia), while other radionuclides (²²⁶Ra, ²²⁸Ra) were not bio-accumulated; (vi) the radionuclide transfer from water to fish was higher compared to the radionuclide transfer from sediment to fish; (vii) the human effective doses due to consumption of Nile tilapia and Manyara tilapia were 0.00973 and 0.005 mSv/y, respectively, which is below the 1 mSv/y international limit. These findings therefore show that the current levels of radioactivity in fish in the study area do not pose a significant radiological risk to fish consumers. However, more studies on other types of fish are recommended. Full article

In order to better understand the extent to which global climate variability is linked to the frequency and intensity of heat waves and overall changes in temperature throughout the United States (US), correlations between long-term monthly mean, minimum, and maximum temperatures throughout the contiguous US on the one hand and low-frequency variability of multiple climate indices (CIs) on the other hand are analyzed for the period from 1948 to 2018. The Pearson’s correlation coefficient is used to assess correlation strength, while leave-one-out cross-validation and a bootstrapping technique (p-value) are used to address potential serial and spurious correlations and assess the significance of each correlation. Three parameters defined the sliding windows over which surface temperature and CI values were averaged: window size, lag time between the temperature and CI windows, and the beginning month of the temperature window. A 60-month sliding window size and 0 lag time resulted in the highest correlations overall; beginning months were optimized on an individual site basis. High (r ≥ 0.60) and significant (p-value ≤ 0.05) correlations were identified. The Western Hemisphere Warm Pool (WHWP) and El Niño/Southern Oscillation (ENSO) exhibited the strongest links to temperatures in the western US, tropical Atlantic sea surface temperatures to temperatures in the central US, the WHWP to temperatures throughout much of the eastern US, and atmospheric patterns over the northern Atlantic to temperatures in the Northeast and Southeast. The final results were compared to results from previous studies focused on precipitation and coastal sea levels. Regional consistency was found regarding links between the northern Atlantic and overall weather and coastal sea levels in the Northeast and Southeast as well as on weather in the upper Midwest. Though the MJO and WHWP revealed dominant links with precipitation and temperature, respectively, throughout the West, ENSO revealed consistent links to sea levels and surface temperatures along the West Coast. These results help to focus future research on specific mechanisms of large-scale climate variability linked to US regional climate variability and prediction potential. Full article

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change. Full article

Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity. Full article

The AquaCrop model is used to predict rice yield in response to different irrigation management in the Yanyun irrigation area in Yangzhou, China, and the constraints to rice production were identified to maximize water productivity based on model simulations. The model was calibrated by comparing measured and predicted canopy cover (CC), yield, and soil water content during the growing season in 2018. The results showed that, for CC simulations, R² was 0.99, RMSE was 3.6%, and NRMSE was 5.3%; for Biomass simulation, RMSE was 0.50 t/ha, and NRMSE was 5.3%. Different irrigation strategies were analyzed for a long-term simulation period from 1955 to 2014. The simulated rice yield increased rapidly as irrigation demand increased initially, and then gradually stabilized. The simulated rice yield fluctuated in the different years. The Pearson type-III model method was used to identify different hydrological years of wet, normal, and dry years. The analysis identified the wet year as 1991, normal year as 1981, and dry year as 1966. In the different rainfall years (1991, 1981, and 1966) water use efficiency (WUE), water productivity (WPet), and irrigation water productivity (IWP) were utilized to determine the irrigation strategy. The predicted highest WPet in the wet year was 1.77kg m⁻³, while the lowest WPet in the dry year was 1.13 kg m⁻³. The highest IWP was 19.78 kg m⁻³ in the wet year, and 9.32 kg m⁻³ in the normal year; while the lowest IWP in the dry year was 1.90 kg m⁻³. IWP was significantly higher in the rainy year, while WUE was significantly lower. On the other hand, WPet was more extensive in the wet year because the yield was higher, and the Evapotranspiration (ET) was smaller in comparison to the dry year. Full article

Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region. Full article

The reintroduction of Eurasian beaver (Castor fiber L.) results in significant changes in ecosystems. The purpose of this study is to assess the impact of the environment-forming activity of C. fiber on the riparian phytocoenoses of the Raifa forest sector of the Volga-Kama State Nature Biosphere Reserve (Middle Volga region, European Russia) after the reintroduction. Phytoindication methods of ecological–coenotic groups and indicator values were used to assess changes in environmental conditions under the influence of beaver activity. The influence of the beaver reintroduction factor on the increase in the moisture regime (by three points according to the Tsyganov indicator values) and the illumination of habitats, the richness of soils in nitrogen, and the acidity and salt regime of soils (by one point) was revealed. Under the conditions of fodder and construction activities of the beaver, an increase in the proportion of aquatic and wetland groups from 10.2% to 28.2% and boreal plant species from 15.0% to 27.6% was detected. An expansive nature of the change in the degree of landscape occupancy with wetland plants was noted. A decrease in the degree of landscape occupancy (3 to 2 points) of the distribution of ruderal species in the riparian zones of the waterbodies of the reserve due to the activity of the beaver was revealed. Based on phytoindication and ecological–coenotic analyses, it was shown that the reintroduction of C. fiber into the waterbodies of the Raifa forest sector of the reserve is responsible for maintaining the necessary microclimatic conditions for the preservation of natural southern boreal communities. The results obtained can be used for predictive assessment of the influence of the beaver on riparian (small rivers and lakes) plant communities of forest ecosystems in the Middle Volga region of European Russia and other regions of the planet with similar environmental conditions. Full article

The Lake Victoria basin’s expanding population is heavily reliant on rainfall and river flow to meet their water needs, making them extremely vulnerable to changes in climate and land use. To develop adaptation and mitigation strategies to climate changes it is urgently necessary to evaluate the impacts of climate change on the quantity of water in the rivers that drain into Lake Victoria. In this study, the semi-distributed hydrological SWAT model was used to evaluate the impact of current land use and climate changes for the period of 1990–2019 and assess the probable future impacts of climate changes in the near future (2030–2060) on the Simiyu river discharge draining into Lake Victoria, Northern Tanzania. The General Circulation Model under RCPs 4.5, 6.0 and 8.5 predicted an increase in the annual average temperature of 1.4 °C in 2030 to 2 °C in 2060 and an average of 7.8% reduction in rainfall in the catchment. The simulated river discharge from the hydrological model under RCPs 4.5, 6.0 and 8.5 revealed a decreasing trend in annual average discharge by 1.6 m³/s from 5.66 m³/s in 2019 to 4.0 m³/s in 2060. The increase in evapotranspiration caused by the temperature increase is primarily responsible for the decrease in river discharge. The model also forecasts an increase in extreme discharge events, from a range between 32.1 and 232.8 m³/s in 1990–2019 to a range between 10.9 and 451.3 m³/s in the 2030–2060 period. The present combined impacts of climate and land use changes showed higher effects on peak discharge at different return periods (Q5 to Q100) with values of 213.7 m³/s (Q5), 310.2 m³/s (Q25) and 400.4 m³/s (Q100) compared to the contributions of climate-change-only scenario with peak discharges of 212.1 m³/s (Q5), 300.2 m³/s (Q25) and 390.2 m³/s (Q100), and land use change only with peak discharges of 295.5 m³/s (Q5), 207.1 m³/s Q25) and 367.3 m³/s (Q100). However, the contribution ratio of climate change was larger than for land use change. The SWAT model proved to be a useful tool for forecasting river discharge in complex semi-arid catchments draining towards Lake Victoria. These findings highlight the need for catchment-wide water management plans in the Lake Victoria Basin. Full article

This study provides, to the best of our knowledge, the first detailed analysis of how surface oil modifies air–sea interactions in a two-way coupled model, i.e., the coupled–ocean–atmosphere–wave–sediment–transport (COAWST) model, modified to account for oil-related changes in air–sea fluxes. This study investigates the effects of oil on surface roughness, surface wind, surface and near-surface temperature differences, and boundary-layer stability and how those conditions ultimately affect surface stress. We first conducted twin-coupled modeling simulations with and without the influence of oil over the Deepwater Horizon (DWH) oil spill period (20 April to 5 May 2010) in the Gulf of Mexico. Then, we compared the results by using a modularized flux model with parameterizations selected to match those selected in the coupled model adapted to either ignore or account for different atmospheric/oceanic processes in calculating surface stress. When non-oil inputs to the bulk formula were treated as being unchanged by oil, the surface stress changes were always negative because of oil-related dampening of the surface roughness alone. However, the oil-related changes to 10 m wind speeds and boundary-layer stability were found to play a dominant role in surface stress changes relative to those due to the oil-related surface roughness changes, highlighting that most of the changes in surface stress were due to oil-related changes in wind speed and boundary-layer stability. Finally, the oil-related changes in surface stress due to the combined oil-related changes in surface roughness, surface wind, and boundary-layer stability were not large enough to have a major impact on the surface current and surface oil transport, indicating that the feedback from the surface oil to the surface oil movement itself is insignificant in forecasting surface oil transport unless the fractional oil coverage is much larger than the value found in this study. Full article

Soil and Water Assessment Tool (SWAT) is a widely used model for runoff, non-point source pollution, and other complex hydrological processes under changing environments (groundwater flow, evapotranspiration, snow melting, etc.). This paper reviews the key characteristics and applications of SWAT. Since its inception in the 1990s, there has been a significant increase in the number of articles related to the SWAT model. In the last 10 years, the number of articles almost reached 4000. The range of applications varies between small and large scales; however, large watershed modelling dominates in North America and Asia. Moreover, the prevailing modelling is related to hydrological impacts in a changing environment, which is a global problem. The significant shortcoming of the SWAT model is the vast quantity of data necessary to run the model to generate accurate and reliable results, which is not accessible in some regions of the world. Apart from its accessibility, it has several advantages, including continuous development, which results in a slew of new interfaces and tools supporting the model. Additionally, it can simulate human activity and agricultural measures and adapt to new circumstances and situations. This article emphasizes weaknesses and strengths of SWAT model application on modelling of hydrological processes in changing climate and environment. Full article

For the past forty years, Ethiopia has been promoting sustainable land management activities to enhance agricultural productivity. This study was intended to identify the factors determining farmers’ adoption and continued use of soil bund measures in the Handosha watershed, Omo-Gibe river basin. A multistage sampling technique was used to select 340 households using the Heckman sample selection model. A total of 235 (69.12%) households adopted soil bunds, but only 89 (37.87%) of them were sustainably practicing soil bunds on their farm plots. Most adopters widely practiced soil bunds (49.42%), followed by stone bund (15.9%), and Fanyajuu (10%). The empirical results of the Heckman sample selection model showed that the farming experience, land tenure security, and perception of profitability of conservation measures were significantly positively affected the adoption of soil bund. Whereas, farm plot size and participation in off farm activities significantly negatively influenced the adoption of soil bund. Sustainable use of soil bund measures were significantly positively influenced by land tenure security, family size, and frequency of extension contact, whereas the distance between farm plots and home, and farm plot size were negatively affected. As a result, a design of agro-ecological-based soil and water conservation (SWC) measures was essential in reducing farmland vulnerability to soil erosion and food insecurity. It has been concluded that conservation practices should not only focus on the implementation and biophysical factors but also consider the socioeconomic interests of the farmers to improve the sustainable use of conservation technologies. Full article

Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring. Full article

Changes in vertical column concentration, size distribution, and surface concentration of aerosol associated with the lockdown imposed by the COVID-19 pandemic in 2020 over the Ahmedabad region in Gujarat State, India, were analyzed. The results are compared with changes over selected Indo-Gangetic Plain (IGP) regions. On 25 March 2020, the prime minister of India declared a complete lockdown throughout the country and later lifted restrictions in a phased manner. Aerosol optical depth (AOD) over the Ahmedabad region on 29 March dropped to as low as 0.11, and in the first two weeks of lockdown, the weekly average AOD was only 0.18. On almost all days of the lockdown period, AOD over the Ahmedabad region was lower than the decadal mean. It was found that the Ahmedabad region responded differently to lockdown conditions compared to the IGP regions. During the first lockdown phase, AOD decreased by about 29% compared to the pre-lockdown period over the Ahmedabad region. However, the average reduction over the IGP was much more, about 50%. The average Angstrom exponent (AE) of 0.96 during the pre-lockdown period over the Ahmedabad region increased phase-wise to 1.36 during the L3 lockdown phase, indicating dominance of fine-mode particles during the lockdown period. It suggests a reduction in anthropogenically produced coarse-mode particles, typically dust produced by vehicular movement, construction, and industrial activities. However, on the other hand, over the IGP region, the high dominance of fine-mode particles during the pre-lockdown period had changed to a high dominance of coarse-mode particles, especially over the Delhi region. This indicates a reduction in anthropogenically produced fine-mode particles, which are mainly generated by fossil and biofuels/biomass combustion, over the IGP region by lockdown conditions. Within a few days of lockdown, PM2.5 was reduced by 64% and 76% over the Ahmedabad and Delhi regions, respectively. The lockdown imposed by the pandemic provided an excellent opportunity to ascertain background aerosol conditions in the atmosphere. Full article