Search Results (384)

Background/Objectives: This study examines how seasonality, pollution, and sample type (water and sediment) influence the presence and distribution of antibiotic resistance genes (ARGs), with a focus on antibiotic resistance genes (ARGs) located on plasmids (the complete set of plasmid-derived sequences, including ARGs) in a tropical urban river. Methods: Samples were collected from three sites along a pollution gradient in the Virilla River, Costa Rica, during three seasonal campaigns (wet 2021, dry 2022, and wet 2022). ARGs in water and sediment were quantified by qPCR, and metagenomic sequencing was applied to analyze chromosomal and plasmid-associated resistance profiles in sediments. Tobit and linear regression models, along with multivariate ordination, were used to assess spatial and seasonal trends. Results: During the wet season of 2021, the abundance of antibiotic resistance genes (ARGs) such as sul-1, intI-1, and tetA in water samples decreased significantly, likely due to dilution, while intI-1 and tetQ increased in sediments, suggesting particle-bound accumulation. In the wet season 2022, intI-1 remained low in water, qnrS increased, and sediments showed significant increases in tetQ, tetA, and qnrS, along with decreases in sul-1 and sul-2. Metagenomic analysis revealed spatial differences in plasmid-associated ARGs, with the highest abundance at the most polluted site (Site 3). Bacterial taxa also showed spatial differences, with greater plasmidome diversity and a higher representation of potential pathogens in the most contaminated site. Conclusions: Seasonality and pollution gradients jointly shape ARG dynamics in this tropical river. Plasmid-mediated resistance responds rapidly to environmental change and is enriched at polluted sites, while sediments serve as long-term reservoirs. These findings support the use of plasmid-based monitoring for antimicrobial resistance surveillance in aquatic systems. Full article

Wet areas, which are locations in the landscape that consistently retain moisture, and channel networks are important landscape compartments, with key hydrological and ecological functions. Hence, defining their spatial boundaries is an important step towards sustainable watershed management. In catchments of developing countries, wet areas and small order channels of river networks are rarely mapped, although they represent a crucial component of local livelihoods and ecosystems. In this study, topographic attributes generated with a 30 m SRTM DEM were used to map wet areas and stream networks of two tropical catchments in Central Brazil. The topographic attributes for wet areas were the local slope and the slope curvature, and the Topographic Wetness Index (TWI) was used to delineate the stream networks. Threshold values of the selected topographic attributes were calibrated in the Santa Maria catchment, comparing the synthetically generated wet areas and drainage networks with corresponding reference (map) features, and validated in the nearby Santa Maria basin. Drainage network and wet area delineation accuracies were estimated using random basin transects and multi-criteria and confusion matrix methods. The drainage network accuracies were 67.2% and 70.7%, and wet area accuracies were 72.7% and 73.8%, for the Santa Maria and Gama catchments, respectively, being equivalent or higher than previous studies. The mapping errors resulted from model incompleteness, DEM vertical inaccuracy, and cartographic misrepresentation of the reference topographic maps. The study’s novelty is the use of readily available information to map, with simplicity and robustness, wet areas and channel initiation in data-scarce, tropical environments. Full article

The prevailing environmental conditions before and during the 28 Large Forest Fires (LFFs) that have occurred in the Canary Islands since 1983 are analyzed. These conditions are often associated with episodes characterized by the advection of continental tropical air masses originating from the Sahara, which frequently result in intense heatwaves. During the onset of the LFFs, the base of the subsidence thermal inversion layer—separating a lower layer of cool, moist air from an upper layer of warm, dry air—is typically located at an altitude of around 350 m above sea level, approximately 600 m below the usual average. Understanding these Saharan air advection events is crucial, as they significantly alter the vertical thermal structure of the atmosphere and create highly conducive conditions for wildfire ignition and spread in the forested mid- and high-altitude zones of the archipelago. Analysis of meteorological records from various weather stations reveals that the average maximum temperature on the first day of fire ignition is 30.3 °C, with mean temperatures of 27.4 °C during the preceding week and 28.9 °C throughout the fire activity period. Relative humidity on the ignition days averages 24.3%, remaining at around 30% during the active phase of the fires. No significant correlation has been found between dry or wet years and the occurrence of LFFs, which have been recorded across years with widely varying precipitation levels. Full article

Paleoclimate records have long documented the existence of multicentury hydroclimate anomalies in the Altiplano of South America. However, the causes and mechanisms of these extended events are still unknown. Here, we present a climate modeling experiment that explores the oceanic drivers and atmospheric mechanisms conducive to long-term precipitation variability in the southern Altiplano (18–25° S; 70–65 W; >3500 masl). We performed a series of 100-year-long idealized simulations using the Weather Research and Forecasting (WRF) model, configured to repeat annually the oceanic and atmospheric forcing leading to the exceptionally humid austral summers of 1983/1984 and 2011/2012. The aim of these cyclical experiments was to evaluate if these specific conditions can sustain a century-long pluvial event in the Altiplano. Unlike the annual forcing, long-term negative precipitation trends are observed in the simulations, suggesting that the drivers of 1983/1984 and 2011/2012 wet summers are unable to generate a century-scale pluvial event. Our results show that an intensification of the anticyclonic circulation along with cold surface air anomalies in the southwestern Atlantic progressively reinforce the lower and upper troposphere features that prevent moisture transport towards the Altiplano. Prolonged drying is also observed under persistent La Niña conditions, which contradicts the well-known relationship between precipitation and ENSO at interannual timescales. Contrasting the hydroclimate responses between the Altiplano and the tropical Andes result from a sustained northward migration of the Atlantic trade winds, providing a useful analog for explaining the divergences in the Holocene records. This experiment suggests that the drivers of century-scale hydroclimate events in the Altiplano were more diverse than previously thought and shows how climate modeling can be used to test paleoclimate hypotheses, emphasizing the necessity of combining proxy data and numerical models to improve our understanding of past climates. Full article

Natural wetlands are critical water quality regulators, especially in developing tropical countries. The Lubigi wetland is a large urban wetland in Kampala, the largest city in Uganda in Africa. We studied whether stormwater discharge and wastewater effluent from a nearby stormwater channel and a sewage treatment plant in the western part of the city were cleaned as they flowed through the wetland. Despite the significant pollution, the wetland removed ammonium-nitrogen, orthophosphate, and particulate nutrients during both seasons, achieving removal rates ranging from 50 to 60% for orthophosphate but only 20–40% for ammonium-nitrogen. Overall, seasonal differences in loads and retention rates of nutrient and organic matter inputs were minimal. Interestingly, the wetland mostly released nitrate and nitrite during water passage through the wetland, most likely due to the mineralization of organic nitrogen and agricultural run-off during rainy events in the wet season. However, the limited capacity of the sewage treatment plant and untreated stormwater discharge from the Nsooba main channel reduced the wetland’s ability to clean water. The insufficient carrying capacity of the treatment plant and the release of untreated sewage into the wetland significantly impact the self-purification capacity of the Lubigi wetland. Thus, the concept of Nature-Based Solutions is ineffective if the wetland systems are overloaded. Full article

Existing wet tropical urban drainage systems often fail to accommodate runoff generated during extreme rainfall. Water-sensitive urban design (WSUD) systems have the potential to retrofit the existing urban drainage system by enhancing infiltration and retention functions. However, studies supporting this assumption were based on temperate or arid climatic conditions, raising questions about its relevance in wet tropical catchments. To answer these questions, in this study a comprehensive modelling study of WSUD effectiveness in a tropical environment was implemented. Engineers Park, a small sub-catchment of 0.27 km² at Saltwater Creek, Cairns, Queensland, Australia was the study site in which the flood mitigation capabilities of grey and WSUD systems under major (1% Annual Exceedance Probability—AEP), moderate (20% AEP), and minor (63.2% AEP) magnitudes of rainfall were evaluated. A detailed one-dimensional (1D) and coupled 1D2D hydrodynamic model in MIKE+ were developed and deployed for this study. The results highlighted that the existing grey infrastructure within the catchment underperformed during major events resulting in high peak flows and overland flow, while minor rainfall events increased channel flow and shifted the location of flooding. However, the integration of WSUD with grey infrastructure reduced peak flow by 0% to 42%, total runoff volume by 0.9% to 46%, and the flood extent ratio to catchment area from 0.3% to 1.1%. Overall, the WSUD integration positively contributed to reduced flooding in this catchment, highlighting its potential applicability in tropical catchments subject to intense rainfall events. However, careful consideration is required before over-generalization of these results, since the study area is small. The results of this study can be used in similar study sites by decision-makers for planning and catchment management purposes, but with careful interpretation. Full article

Understanding the seasonal regulation of transpiration and stomatal conductance is critical for evaluating plant water-use strategies in response to environmental variability. This study assessed these physiological traits in three dominant savanna tree species (Piliostigma thonningii (Schumach.) Milne-Redh., Combretum molle R.Br. ex G.Don, and Balanites aegyptiaca (L.) Delile) in Ruma National Park, Kenya. Measurements were taken during wet and dry seasons under varying canopy light conditions (light-exposed vs. shaded leaves) and soil moisture regimes. A randomized design with four treatments and three replicates was employed. Results showed significantly higher transpiration and stomatal conductance during wet seasons, especially in sunlit leaves (p < 0.05). P. thonningii exhibited the highest rates of transpiration (9 mmol m⁻² s⁻¹) and stomatal conductance (~2.2 mmol m⁻² s⁻¹) in light conditions, while B. aegyptiaca maintained consistently low values, reflecting a drought-tolerant strategy. C. molle demonstrated intermediate responses, suggesting a balance between water conservation and resource use. Despite seasonal trends, low R² values indicated that internal physiological regulation outweighed the influence of external climatic drivers. These findings reveal species-specific water-use strategies and highlight the ecological significance of leaf-level responses to light and moisture availability in tropical savannas. The study provides valuable insights for forest management and climate-resilient restoration planning in water-limited ecosystems. Full article

Tropical glaciers are highly sensitive to climate change, with their mass balance influenced by temperature and precipitation, which affects the accumulation area. In this study, we developed an open-source tool to map the accumulation area of glaciers in the Cordillera Blanca, Peru (1988–2023), using Landsat images, spectral indices, and the Otsu method. We analyzed trends and correlations between snow accumulation area, meteorological patterns from ERA5 data, and oscillation modes. The results were validated using field data and manual mapping. Greater discrepancies were observed in glaciers with debris cover or small clean glaciers (<1 km²). The Amazonian and Pacific sectors showed a significant trend in decreasing accumulation areas, with reductions of 8.99% and 10.24%, respectively, from 1988–1999 to 2010–2023. El Niño events showed higher correlations with snow accumulation, snowfall, and temperature during the wet season, indicating a stronger influence on the Pacific sector. The accumulation area was strongly anti-correlated with temperature and correlated with snowfall in both sectors at a 95% confidence level (α = 0.05). The highest correlations with meteorological parameters were observed during the dry season, suggesting that even minor changes in temperature or precipitation could significantly impact the accumulation area. Full article

In semi-arid regions, seasonally dry tropical forests are essential for regulating the surface energy balance, which can be analyzed by examining air heating processes and water availability control. The objective of this study was to evaluate the ability of the Brazilian Developments on the Regional Atmospheric Modelling System (BRAMS) model in simulating the seasonal variations of the energy balance components of the Caatinga biome. The surface measurements of meteorological variables, including air temperature and relative humidity, were also examined. To validate the model, we used data collected in situ using an eddy covariance system. In this work, we used the BRAMS model version 5.3 associated with the Joint UK Land Environment Simulator (JULES) version 3.0. The model satisfactorily represented the rainfall regime over the northeast region of Brazil (NEB) during the wet period. In the dry period, however, the coastal rainfall pattern over the NEB region was underestimated. In addition, the results showed that the surface fluxes linked to the energy balance in the Caatinga were impacted by the effects of rainfall seasonality in the region. The assessment of the BRAMS model’s performance demonstrated that it is a reliable tool for studying the dynamics of the dry forest in the region, providing valuable support for sustainable management and conservation efforts. Full article

Sri Lanka typically experiences anomalously wet conditions during the summer following El Niño events, but this response varies due to El Niño complexity. This study investigates the impact of post-El Niño conditions on Sri Lanka’s Monsoon rainfall, contrasting summers after fast- and slow-decaying El Niño events. Results indicate that fast-decaying El Niño events lead to wet and cool summers while slow-decaying events result in dry and warm summers. These contrasting responses are linked to sea surface temperature (SST) changes in the central to eastern Pacific. During the fast-decaying El Niño, the transition to La Niña generates strong easterlies in the central and eastern Pacific, enhancing moisture convergence, upward motion, and cloud cover, resulting in wetter conditions over Sri Lanka. During the fast-decaying El Niño, enhanced precipitation over the Maritime Continent acts as a diabatic heating source, inducing Gill-type easterly wind anomalies over the tropical Pacific. These winds promote coupled feedbacks that accelerate the transition to La Niña, strengthening moisture convergence and upward motion over Sri Lanka. Conversely, slow-decaying El Niño events are associated with cooling in the western North Pacific and warming in the Indian Ocean, which promotes the development of the western North Pacific anticyclone, suppressing upward motion and reducing cloud cover, leading to conditions over Sri Lanka. Changes in the Walker circulation further contribute to these distinct rainfall patterns, highlighting its influence on regional climate dynamics. These findings enhance our understanding of the seasonal predictability of rainfall in Sri Lanka during post-El Niño Summers. Full article

Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with ${\overline{R}}^2$ values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The ${\overline{R}}^2$ values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models. Full article

Rapid land-use changes have significantly changed the occurrence of heavy metals (HMs) in tropical watershed systems. However, the influence of land-use patterns on the spatial and temporal distribution of HMs in tropical river systems remains poorly understood. This study aims to explore the relationship between land-use types and HM pollution in the China’s largest tropical watershed, the Nandu River. Eight heavy metals (Cd, Pb, Cr, Cu, Zn, As, Hg, and Sb) in the surface water were monitored across river, estuary, and nearshore zones during wet and dry seasons. Our findings show a higher total concentration of eight heavy metals (ΣHMs) in the wet season (30.52 μg/L) compared to the dry season (21.53 μg/L). In the wet season, ΣHM concentrations followed the order: estuary (70.96 μg/L) > basin (31.03 μg/L) > nearshore (8.07 μg/L). In the dry season, it was basin (31.56 μg/L) > estuary (23.26 μg/L) > nearshore (7.49 μg/L). Land-use patterns had higher interpretation rates for HM distribution in the dry season (65.8–73.0%) compared to the wet season (31.0–42.4%). The 2000 m buffer zone had a greater impact on HM distribution than the 500 m and 1000 m zones. Agricultural land and construction areas were the primary contributors to HM pollution in the dry and wet seasons, respectively. Noteworthy, in the river basin, chromium (Cr) presented carcinogenic risks to both children and adults through ingestion in both seasons and arsenic (As) posed a risk to children in the dry season. This study provides valuable insights for the sustainable management of land use and improving river water quality by highlighting the relationship between land use and HM contamination in tropical river ecosystems. Full article

Soil erosion poses a significant global environmental challenge, causing land degradation, deforestation, river siltation, and reduced agricultural productivity. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely applied in Brazil, its use in the tropical river basins of the Amazon remains limited. This study aimed to apply a GIS-integrated RUSLE model and compare its soil loss estimates with multiple linear regression (MLR) models based on terrain attributes, aiming to identify priority areas and key geomorphometric drivers of soil erosion in a tropical Amazonian river basin. A digital elevation model based on Shuttle Radar Topography Mission (SRTM) data, land use and land cover (LULC) maps, and rainfall and soil data were applied to the GIS-integrated RUSLE model; we then defined six risk classes—slight (0–2.5 t ha⁻¹ yr⁻¹), slight–moderate (2.5–5), moderate (5–10), moderate–high (10–15), high (15–25), and very high (>25)—and identified priority zones as those in the top two risk classes. The Caeté River Basin (CRB) was classified into six erosion risk categories: low (81.14%), low to moderate (2.97%), moderate (11.88%), moderate to high (0.93%), high (0.03%), and very high (3.05%). The CRB predominantly exhibited a low erosion risk, with higher erosion rates linked to intense rainfall, gentle slopes covered by Arenosols, and human activities. The average annual soil loss was estimated at 2.0 t ha⁻¹ yr⁻¹, with a total loss of 1005.44 t ha⁻¹ yr⁻¹. Additionally, geomorphological and multiple linear regression (MLR) analyses identified seven key variables influencing soil erosion: the convergence index, closed depressions, the topographic wetness index, the channel network distance, and the local curvature, upslope curvature, and local downslope curvature. These variables collectively explained 26% of the variability in soil loss (R² = 0.26), highlighting the significant role of terrain characteristics in erosion processes. These findings indicate that soil erosion control efforts should focus primarily on areas with Arenosols and regions experiencing increased anthropogenic activity, where the erosion risks are higher. The identification of priority erosion areas enables the development of targeted conservation strategies, particularly for Arenosols and regions under anthropogenic pressure, where the soil losses exceed the tolerance threshold of 10.48 t ha⁻¹ yr⁻¹. These findings directly support the formulation of local environmental policies aimed at mitigating soil degradation by stabilizing vulnerable soils, regulating high-impact land uses, and promoting sustainable practices in critical zones. The GIS-RUSLE framework is supported by consistent rainfall data, as verified by a double mass curve analysis (R² ranging from 0.64 to 0.77), and offers a replicable methodology for soil conservation planning in tropical basins with similar erosion drivers. This approach offers a science-based foundation to guide soil conservation planning in tropical basins. While effective in identifying erosion-prone areas, it should be complemented in future studies by dynamic models and temporal analyses to better capture the complex erosion processes and land use change impacts in the Amazon. Full article

Cities worldwide face significant challenges in managing stormwater, a concern worsened by rapid urbanization and the impacts of climate change. Bioretention landscapes helped solve these issues by replicating natural ecosystems to effectively capture, filter, and treat stormwater while offering additional ecosystem services. However, most studies and existing guides have been for colder and drier climates. Adapting bioretention practices to tropical and wet equatorial climates, characterized by intense rainfall patterns and high temperature and humidity, presents unique challenges and knowledge gaps. This systematic literature review aims to address these gaps by synthesizing existing research from 2010 to 2022 on bioretention landscapes in tropical and wet equatorial climates. Following the methodology outlined in PRISMA guidelines, we identified 10 key studies primarily focusing on countries within the Köppen–Geiger climate zones Aw, Af, and Am, which are tropical and wet equatorial climates. These studies spanned across different continents, including locations such as Malaysia, Singapore, Burkina Faso, and India. Data synthesis revealed critical design elements, including planting selection, substrate layer composition, and performance metrics. Our findings highlight the necessity for climate-specific design approaches and identify key research gaps that can inform future studies and guide practical applications in designing bioretention landscapes for tropical and wet equatorial climates. Full article

Statistical cluster analysis applied to monthly 1–100 m ocean temperatures reveals El Niño–Southern Oscillation (ENSO) dipole patterns with a leading mode having opposing centers of action across the dateline and tropical east Pacific. We focus on the La Niña cold phase and study its impact on the Caribbean climate over the period of 1980–2024. East dipole time scores are used to identify composite years, and anomaly patterns are calculated for Jan-Jun and Jul-Dec. Convective responses over the Caribbean exhibit seasonal contrasts: dry winter–spring and wet summer–autumn. Trade winds and currents across the southern Caribbean weaken and lead to anomalous warming of upper ocean temperatures. Sustained coastal upwelling off Peru and Ecuador during east La Niña is teleconnected with easterly wind shear and tropical cyclogenesis over the Caribbean during summer, leading to costly impacts. This ocean–atmosphere coupling is quite different from the more common central Pacific ENSO dipole. Full article