Search Results (108)

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning. Full article

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. 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

The Palearctic region is characterised by high endemism in the west and east, and a low endemism centre. The endemic lineages occurring at the two ends are largely distinct, and eastern endemics are typically associated with humid climates and forests, representing the start of a continuum from temperate to tropical forest groups and leading to Indo-Malay endemics. In contrast, western Palearctic endemics are typically associated with arid or seasonally dry (Mediterranean) climates and vegetation. Those lineages occurring in the central Palearctic are typically of western origin. Here, we use phylogenetic age (older than 34 million years (My)) to define a list of tetrapod and vascular plant lineages endemic to the western and central Palearctic, map their distributions at the ecoregion scale, and combine these maps to illustrate and understand lineage richness and endemism patterns. Sixty-three ancient lineages were recovered, approximately half of them reptiles, with several herbaceous and shrubby angiosperms, amphibians, and rodents, and single lineages of woody conifers, insectivores, and birds. Overall, we show high lineage richness in the western Mediterranean, eastern Mediterranean, and Iran, with the highest endemism values recorded in the western Mediterranean (southern Iberian Peninsula, southern France). This paints a picture of ancient lineage survival in areas of consistently dry climate since the Eocene, but also in association with persistent water availability (amphibians in the western Mediterranean). The almost complete absence of ancient endemic bird lineages is unusual and perhaps unique among the world’s biogeographic regions. The factors accounting for these patterns include climate since the end of the Eocene, micro-habitats and micro-climates (of mountain terrain), refugia, and patchiness and isolation (of forests). Despite their aridity adaptations, some of the lineages listed here may be tested under anthropogenic climatic change, although some may extend into the eastern Palearctic. We recommend using these lineages as flagships for conservation in the study region, where their uniqueness and antiquity deserve greater recognition. 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

Mangrove forests are among the most productive vascular plants on Earth. The gross (GPP) and aboveground forest net primary production (ANPP) correlate positively with precipitation. ANPP also correlates inversely with porewater salinity. The main drivers of the forest primary production are the porewater salinity, rainfall, tidal inundation frequency, light intensity, humidity, species age and composition, temperature, nutrient availability, disturbance history, and geomorphological setting. Wood production correlates positively with temperature and rainfall, with rates comparable to tropical humid forests. Litterfall accounts for 55% of the NPP which is greater than previous estimates. The fine root production is highest in deltas and estuaries and lowest in carbonate and open-ocean settings. The GPP and NPP exhibit large methodological and regional differences, but mangroves are several times more productive than other coastal blue carbon habitats, excluding macroalgal beds. Mangroves contribute 4 to 28% of coastal blue carbon fluxes. The mean and median canopy respiration equate to 1.7 and 2.7 g C m⁻² d⁻¹, respectively, which is higher than previous estimates. Mangrove ecosystem carbon fluxes are currently in balance. However, the global mangrove GPP has increased from 2001 to 2020 and is forecast to continue increasing to at least 2100 due to the strong fertilization effect of rising atmospheric CO₂ concentrations. Full article

This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. Among the 324 TCs within the domain, 81 were identified as RI TCs, exhibiting a 24-h intensity increase of at least 15 ms⁻¹ at least once in their lifetime. Environmental variables used for the input matrix are extracted from the nearest grid cell corresponding to each RI and non-RI event’s geographic location and time of occurrence. Additionally, the geographic location of each event and its initial intensity positions (24-h prior) are also included in the model. The XGBC, with 10-fold cross-validation, became the optimum classifier by achieving the highest classification accuracy, as well as the lowest probability of false detection and the highest AUC score on the unseen data. The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. This study will advance storm preparedness strategies for the SWPO nations through correctly predicting RI-TCs and prioritizing early prediction of contributing environmental variables. Full article

Human-caused habitat conversion, degradation, and climate change threaten global biodiversity, particularly in tropical forests where vascular epiphytes—non-parasitic plants growing on other plants—may be especially vulnerable. Epiphytes play vital ecological roles, in nutrient cycling and by providing habitat, but are disproportionately affected by land-use changes due to their reliance on host trees and specific microclimatic conditions. While tree species in secondary forests recover relatively quickly, epiphyte recolonization is slower, especially in humid montane regions, where species richness may decline by up to 96% compared to primary or old-growth forests. A review of nearly 300 pertinent studies has revealed a geographic bias toward the Neotropics, with limited research from tropical Asia, Africa, and temperate regions. The studies can be grouped into four main areas: 1. trade, use and conservation, 2. ecological effects of climate and land-use change, 3. diversity in human-modified habitats, and 4. responses to disturbance. In agricultural and timber plantations, particularly those using exotic species like pine and eucalyptus, epiphyte diversity is significantly reduced. In contrast, most native tree species and shade-grown agroforestry systems support higher species richness. Traditional polycultures with dense canopy cover maintain up to 88% of epiphyte diversity, while intensive management practices, such as epiphyte removal in coffee and cacao plantations, cause substantial biodiversity losses. Conservation strategies should prioritize preserving old-growth forests, maintaining forest fragments, and minimizing intensive land management. Active restoration, including the translocation of fallen epiphytes and planting vegetation nuclei, is more effective than passive approaches. Future research should include long-term monitoring to understand epiphyte dynamics and assess the broader impacts of epiphyte loss on biodiversity and ecosystem functioning. Full article

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R² = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R² = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R² = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R² = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R² = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R² = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change. Full article

Global change influences species diversity patterns. Compared with seed plants, ferns are more sensitive to temperature and humidity changes and are an ideal group for studying species diversity patterns under future climate changes. Polypodiaceae, which has important ecological and application value, such as medicinal and ornamental value, is one of the most widely distributed fern families, with rich species diversity. Here, we explore the changes in the species diversity patterns of Polypodiaceae and their influencing factors. We collected more than 300,000 data points on the distribution of Polypodiaceae to map actual current species diversity patterns. We used Maxent to establish current and future potential species distribution models using 20 predictors and determined the current species diversity patterns using the actual current species diversity patterns and current potential species distribution model method. Multiple linear regression and random forest models were used to evaluate the effects of climate factors on the species diversity patterns of Polypodiaceae. We evaluated the effects of future climate changes on the species diversity of Polypodiaceae. The species diversity of Polypodiaceae increased gradually from higher to lower latitudes and the centers were concentrated in the low latitudes of tropical rainforests. There were four distribution centers across the world for Polypodiaceae: central America, central Africa, southern Asia, and northern Oceania. The species diversity of Polypodiaceae was greatly affected by precipitation factors rather than temperature factors. Under future climate change scenarios, species diversity is expected to shift and accumulate toward the equator in mid-to-low latitudes. Species diversity is projected to remain concentrated in low-latitude regions but will tend to aggregate towards higher altitude areas as global temperatures rise, with precipitation during the warmest season identified as the most influential factor. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article

The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production. Full article

This study presents a high-resolution palaeoclimate reconstruction based on a radiocarbon-dated 240 cm deep trench profile from Renuka Lake, Northwestern Himalaya, India. The palynological analysis provides insight into the palaeovegetation and palaeoclimatic dynamics of a subtropical, dense, mixed deciduous forest, predominantly characterized by Sal (Shorea robusta). The fossil pollen reveals the presence of tropical Sal mixed deciduous taxa, including Shorea robusta, Emblica officinalis, Murraya koenigii, Toona ciliata, Syzygium cumini, and Terminalia spp., which indicate that the region experiences a warm and humid climate with the strong Indian Summer Monsoon (ISM) during ~7500–4460 cal yr BP. Subsequently, Sal-mixed deciduous forests were replaced by highland taxa, viz., Pinus roxburghii and Abies pindrow, suggesting dry and cold conditions during ~4460–3480 cal yr BP. Additionally, warm and humid (~3480–3240, ~3060–2680, ~2480–2270 cal yr BP) and cold and dry conditions (~3240–3060, ~2680–2480, ~2270–1965 cal yr BP) recorded alternatively in this region. Improved ISM prevailed ~1965–940 cal yr BP, followed by cold and dry conditions ~940–540 cal yr BP. From ~540 cal yr BP to present, the appearance of moist deciduous taxa alongside dry deciduous and highland taxa in similar proportions suggests moderate climate conditions in the region. Environmental reconstructions are supported by the Earth System Palaeoclimate Simulation (ESPS) model, providing an independent validation of the pollen-based interpretations. This research contributes to our understanding of long-term vegetation dynamics in the Northwestern Himalaya and offers valuable insights into the historical variability of the Indian Summer Monsoon, establishing a foundation for future investigations of climate-driven vegetation changes in the region. Full article

Forest types correspond to differences in structural characteristics and species composition that influence biomass and biodiversity values, which are essential measurements for ecological monitoring and management. However, differentiating forest types in tropical regions remains a challenge. This study aimed to improve forest type extent mapping by combining structural information from discrete full-waveform LiDAR returns with multitemporal images. This study was conducted in a tropical forest region over complex terrain in north-eastern Tanzania. First, structural classes were generated by applying time-series clustering algorithms. The results showed four different structural clusters corresponding to forest types, montane–humid forest, montane–dry forest, submontane forest, and non-forest, when using the Kshape algorithm. Kshape considers the shape of the full-sequence LiDAR waveform, requiring little preprocessing. Despite the overlap amongst the original clusters, the averages of structural characteristics were significantly different across all but five metrics. The labeled clusters were then further refined and used as training data to generate a wall-to-wall forest cover type map by classifying biannual images. The highest-performing model was a KNN model with 13 spectral and 3 terrain features achieving 81.7% accuracy. The patterns in the distributions of forest types provide better information from which to adapt forest management, particularly in forest–non-forest transitional zones. Full article