Global Climate Models (GCMs) offer valuable seasonal precipitation forecasts information. However, their predictive performance may be inferior to traditional climatological forecasts derived from historical precipitation data. In this study, we evaluate the spatiotemporal skill of calibrated GCMs across China to determine whether their skill surpasses climatological forecasts at various lead times. Six GCMs are statistically calibrated using a Gamma-Gaussian model and integrated via Bayesian Model Averaging. The calibrated GCMs forecast is then compared with the climate forecast for different climate zones and months, and in the formulation of actual meteorological business and academic research, integer months are often used to describe the forecast period, and the preparation time is 1 month, 2 months, and 3 months. The results indicate that for the one-month lead time, the skill of calibrated GCM forecasts outperforms climatological forecasts in 33% (322/971) of grid cells. However, the skill of calibrated GCMs declines with longer lead times, with only 24% and 20% of grid cells surpassing the climatological forecasts at two- and three-month lead times, respectively. Regionally, the calibrated GCMs forecasts exhibit stronger superiority over climatological forecasts in the Northern Subtropical Zone than in other climate zones, while showing the most limited improvement compared to climatological benchmarks in the Middle Temperate Zone. Seasonally, the skill advantages of the calibrated forecasts relative to climatological forecasts are more pronounced during the non-flood season (September to March) than during the flood season (April to August). The average proportions of grid cells during the flood season are 29%, 18%, and 18% across the three lead times, compared to 49%, 34%, and 27% during the non-flood season. Overall, this study provides a comprehensive evaluation of the skill of calibrated GCMs across China, offering a framework for assessing their effectiveness in delivering reliable seasonal precipitation forecasts in other regions. The online version contains supplementary material available at 10.1038/s41598-026-39636-8.
The present study investigates quarterly variations of indoor radon (222Rn) and thoron (220Rn) concentrations. It derives the quarterly and seasonal correction factors (QCFs and SCFs) in four different specific climatological areas of Cameroon. The passive type detectors based on CR39, commercially known as RADUET, were used. Detectors were placed at each area and replaced every three months throughout the year to measure indoor concentrations of radon and thoron and to observe their fluctuations over the year. The results obtained reveal significant seasonal variability, with the highest radon and thoron levels generally observed during the rainy and harmattan seasons, attributed to reduced ventilation, and increased soil moisture. Annual average radon concentrations ranged from 70 Bq m-3 in Yaoundé to 137.5 Bq m-3 in Ngaoundéré, the latter exceeding the national radon average of 103 Bq m-3. Calculated QCFs for radon exhibited a wide range across the studied regions, from 0.42 (Maroua, Q2) to 1.68 (Ngaoundéré, Q3). Similarly, seasonal SCFs for radon varied from 0.47 (Maroua, dry season) to 1.49 (Maroua, rainy season). Nevertheless, thoron correction factors showed less pronounced but still significant variations, reflecting its shorter half-life. These factors are strongly influenced by local climatological conditions, and geological background. These findings provide important data for the assessment of indoor radon and for addressing the public health strategies and building regulations aimed at mitigating indoor radon and thoron risks in Cameroon and in similar tropical regions.
The increasing frequency and severity of global droughts present critical challenges to agriculture and climate resource management, highlighting the need for comprehensive drought data. To address this, we introduce the Near-global Agro-Climatological Drought Monitoring (NEC-DROMO) dataset, which integrates key agro-climatological variables to provide robust drought insights. Spanning 2002-2021 at a 0.25-degree spatial resolution and monthly scale, NEC-DROMO combines soil moisture (SM) and vegetation water content (VWC) to capture agricultural stress, with rainfall and temperature for climatological context. NEC-DROMO delivers monthly drought data for near-global regions, with latency depending on the availability of input datasets. NEC-DROMO is built through Principal Component Analysis, which objectively weights each variable, ensuring robustness for historical monitoring and potential near-real-time applications. Validation against independent satellite-based and in-situ methods, including the SPI, NDVI, and Geocoded Disaster dataset, confirms NEC-DROMO's reliability in accurately capturing global drought patterns. Building upon the ECoHydrological Land Reanalysis dataset and using its VWC, SM, and temperature data, NEC-DROMO represents a significant advancement in global drought monitoring, supporting policymaking and disaster risk assessment.
Soil moisture (SM) has long been recognized as one of the essential climate variables. However, there are no satellite-derived global SM climatological records spanning over 30 years at fine spatiotemporal resolution. Based on the latest version of the 25 km CCI SM data and 1 km soil data derived from the SoilGrids, a spatially continuous CCI dataset was first obtained using the triple collocation analysis algorithm with the assistance of SM derived from the ERA5, GLEAM, and GLDAS. Subsequently, SM was derived at a fine resolution of 1 km using a downscaling approach considering the sub-grid variability of hydraulic parameters. Finally, the first satellite-derived SM climatological record (1980-2023) at fine spatiotemporal resolution with global seamless coverage was developed. A comprehensive validation against 372 global in-situ observations yielded an averaged unbiased root mean square error of 0.051 m3/m3. Specifically, a Bayesian statics-based credibility analysis of the validated accuracy was first conducted at four networks with dense observation stations. Results indicated an averaged credibility of 83.5% of the accuracy assessment at these networks.
This study investigates bioclimatic comfort conditions in Iran by integrating perspectives from traditional Persian Medicine (PM) with modern climatological methodologies. The primary objective is to develop the Persian Medicine (PMI) as a criterion for evaluating comfort zones across Iran and to compare its results with modern climatological indices, specifically the Terjung Index (TI) and the Standard Effective Temperature Index (SETI). Daily meteorological data from 2003 to 2018 were utilized. To construct the PMI, qualitative variables describing temperate regions were extracted from classical PM texts and implemented Python programming, Excel and ArcGIS 10.8 for spatial analysis. The analysis focuses on May, September, and December, months that exhibited the highest degree of similarity among the comfort zones identified by PMI, TI, and SETI. Results indicate that the spatiotemporal distribution of comfort zones derived from SETI corresponds most closely with those identified by PMI, representing the highest levels of climatic and physiological comfort throughout the year. Overall, the findings demonstrate a strong correspondence between modern climatological comfort indices and PMI-based assessments in temperate regions of Iran. This research highlights the potential of integrating traditional medical- environmental knowledge with modern climate science, providing valuable insights for medical geography, environmental health management, and climate adaptation planning under global warming conditions.
The timing of spring onset is a widely used indicator of climate change impacts, yet estimates of phenological trends depend critically on the choice of reference climate period. Using long-term daily air temperature records from 21 meteorological stations across Estonia, this study examines how different climatological normals influence estimates of spring onset timing and its change. Spring onset was defined as the sustained transition of mean daily air temperature above 5 °C and analysed across four overlapping reference periods (1965-1990, 1971-2000, 1981-2010, and 1991-2020). Mean spring onset dates vary substantially among reference periods, demonstrating that the concept of an "average" spring is sensitive to the selected normal. Cumulative changes derived from linear trends reveal a widespread advancement of spring onset over 1965-2020, with particularly strong shifts at several inland and coastal stations. When contrasting equal-length 30-year normals (1971-2000 vs. 1991-2020), trend magnitudes are generally weaker in the most recent period and, at some stations, near zero or positive. Comparison across reference periods indicates pronounced temporal heterogeneity in phenological responses. While some stations exhibit strong advances during the late twentieth century followed by weakening in the most recent climate normal, others show sustained strong change or weak and inconsistent responses. These patterns cannot be adequately described by a single linear trend and do not follow a simple geographic gradient. The results demonstrate that reference period selection can substantially affect both the magnitude and interpretation of phenological trends. It is therefore recommended that phenological studies explicitly report reference periods and, where possible, compare trend estimates across multiple climatological normals to improve the robustness and transparency of climate impact assessments.
Stratospheric ozone (O3) losses and consequent increases in surface ultraviolet (UV) radiation remain a health concern due to close association with skin cancers and cataracts. This study estimated how emissions of ozone-depleting substances (ODS) affect, via O3 reductions, surface UV radiation and associated health impacts in Australia compared to the United States. We used climatological atmospheric data and the U.S. Environmental Protection Agency's Atmospheric Health Effects Framework (AHEF) model to estimate these effects for historic and projected ODS emissions. Compared to the United States, the Australian population is potentially exposed to ca. 24% more biologically weighted UV radiation because of the closer proximity of its population to the equator, less stratospheric O3 at comparable latitudes, and seasonal variations in the Earth-Sun distance. Surface UV increments owing to ODS-caused stratospheric O3 depletion were largest in the 1990s, while the estimated health impacts peak several decades later at ca. 0.5% for cataracts, 1% for melanoma incidence, and 5% for keratinocyte cancer incidence (relative to 1980), and are similar in both countries. The study found that differences in population-weighted surface UV can only partly explain Australia's substantially higher incidence and mortality rates for UV-related maladies. Other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role. Ozone in the upper atmosphere helps block harmful ultraviolet (UV) radiation from reaching Earth's surface. Chemicals known as ozone‐depleting substances (ODS) can reduce ozone levels, allowing more UV radiation through. Increased UV exposure increases the risk of health problems like skin cancer and cataracts. The amount of UV that reaches the surface varies by location. In this study, we used a model to compare UV exposure and resulting skin cancer and cataract rates between the United States and Australia. Australians receive about 24% more harmful UV radiation under clear skies. Our study explains that this is due mostly to Australia's closer proximity to the equator, and partly due to climatological differences in stratospheric ozone and seasonal variations in the Earth‐Sun distance. We further estimate that ozone depletion has increased incidence rates of UV‐induced illness similarly in both Australia and the USA, and by less than 5 percent. Overall, the study found that differences in UV radiation only partly explain the significantly higher rates of UV‐induced illness in Australia than in the USA, suggesting that other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role.
Heatwaves are becoming more frequent and intense in Bangladesh, particularly in urban areas such as Dhaka, where the combined effects of extreme heat and humidity pose serious public health risks. Temperature-based warning systems frequently underestimate physiological heat stress. This study proposes a machine-learning framework to estimate and project the Heat Index (HI) using 3-hourly meteorological records from 2014 to 2023. Because HI is a deterministic function of air temperature and relative humidity, the framework performs conditional estimation of HI based on meteorological predictors rather than independently forecasting HI itself. Five models: ARIMAX, SARIMAX, Random Forest Regressor (RFR), XGBoost, and Long Short-Term Memory (LSTM), were trained using air temperature, relative humidity, and seasonal indicators as predictors. For scenario-based projections beyond 2023, future temperature and humidity were approximated using historical monthly averages, generating scenario-based HI projections that preserve seasonal and diurnal patterns. These projections represent climatological scenarios rather than true meteorological forecasts. The Random Forest Regressor (RFR) achieved the highest conditional estimation accuracy, with the lowest RMSE (0.85°C) and highest R² (0.987) on the test set. Empirical 95% prediction intervals achieved 98.85% coverage, indicating slightly conservative uncertainty bounds. Scenario-based projections yielded mean HI values of 29.02°C (optimistic), 29.90°C (moderate), and 31.33°C (pessimistic). A substantial proportion of projected 3-hourly periods fall within the "Extreme Caution" category (32-41°C), indicating persistently elevated heat-stress exposure under climatological assumptions. The proposed framework demonstrates strong potential for generating high-resolution scenario-based HI projections by capturing nonlinear temporal dynamics and sub-daily variability. These findings can support scenario-based early warning systems and inform adaptive urban heat-management strategies in climate-vulnerable cities such as Dhaka, although results should be interpreted as conditional projections rather than deterministic forecasts. Unlike conventional HI studies, this framework translates meteorological inputs into high-resolution, operational heat-risk insights by modeling temporal persistence at sub-daily scales.
Malaria remains a significant public health issue in sub‑Saharan Africa, influenced by climate variability, population movement and urbanisation. In South Africa, efforts to eliminate malaria operate within a dynamic environment where climatic fluctuations and population movements complicate risk patterns. However, existing approaches to analyzing climate-malaria relationships, often rely on seasonal averages, ignoring the potential influence of rare climatic extremes and migration flows. This study investigates how climatic extremes, climatic variability, and migration flows interact to shape malaria risk and inform adaptive surveillance and control strategies. Climate data (monthly minimum/maximum temperature, rainfall and relative humidity) for Limpopo, Mpumalanga and KwaZulu‑Natal (2000-2024) were obtained from the South African Weather Service. Laboratory‑confirmed malaria cases were sourced from provincial surveillance reports, and in‑migration data into Thembelihle and selected parts of Soweto (2017-2024) were obtained from the Soweto and Thembelihle Health and Demographic Surveillance System (SaT‑HDSS). Seasonal climate trajectories were evaluated using Ordinary Least Squares regressions. Climatic seasonality was assessed using diagnostic metrics, humidity plateaus, rainfall extremes and thermal season‑length indices. Shock‑year dynamics (KwaZulu‑Natal 2022 floods; Limpopo 2017 Cyclone Dineo) were analysed using monthly anomalies against 2000-2010 climatological envelopes. Pearson correlations examined same‑year and lag‑1 climate-malaria associations. Climate-malaria-migration co‑movement was assessed using min-max‑normalised rainfall, humidity, temperature, incidence and migration data (0-1). Malaria tail events were defined as years at or above the provincial 90th percentile. All statistical analyses were conducted using IBM Statistical Package for the Social Sciences (SPSS), Version 30; p < 0.05 was considered significant. Climate trends across the three endemic provinces showed consistent minimum-temperature warming, declining warm-season humidity, and rainfall patterns increasingly dominated by short-duration extremes. Province-specific seasonality diagnostics revealed that KwaZulu-Natal experienced frequent multi-month humidity plateaus, Limpopo showed pronounced lagged hydrological effects following extreme rainfall events, and Mpumalanga displayed prolonged warm-season durations in recent years. Shock-year analyses (KwaZulu-Natal 2022 floods; Limpopo 2017 Cyclone Dineo) demonstrated compound climatic anomalies, combinations of intense rainfall, sustained humidity, and sub-seasonal cooling that extended environmental suitability beyond expected seasonal boundaries. Same-year humidity was positively associated with malaria incidence in KwaZulu-Natal and Mpumalanga, whereas lag-1 rainfall showed a significant relationship with malaria in Limpopo, reflecting hydrological carry-over effects. Following the April-May 2022 floods, migration into Thembelihle increased markedly, and a co-movement analysis indicated temporal alignment between malaria tail years, elevated rainfall, higher minimum temperatures, and increased migration during climatologically disrupted periods. Climatic extremes and post‑shock mobility reorganise malaria risk in South Africa. Tail‑aware, lag‑sensitive and mobility‑integrated surveillance is essential for adaptive elimination planning.
Heat stress limits for human survivability have been previously defined by a 6-hour exposure to a wet-bulb temperature of 35oC. However, the recently developed physiology-based HEAT-Lim model demonstrates that environmental heat stress thresholds may be cooler and drier than previously thought. We employ HEAT-Lim to determine whether non-survivable thresholds were surpassed during six historical events where conditions were climatologically extreme and/or high heat-related mortality was reported. Our results show that non-survivable conditions are occurring during present-day heat events, all of which are below 35oC wet-bulb temperature. Of concern is regular exceedances of deadly thresholds for older people directly exposed across all events. Moreover, extremely hot yet dry conditions are found to be just as deadly as hot and humid conditions. For future climatological assessments, we emphasise the importance of employing increasingly accurate physiology-derived methods to assess the risk of potentially deadly heat stress.
Amazonian floodplain lakes rank among the most dynamic aquatic ecosystems globally, yet their monitoring is hampered by extreme hydrological variability, high turbidity, and optical complexity. We investigated chlorophyll-a (Chl-a) dynamics in Lago Grande de Curuai, a large floodplain lake hydraulically connected to the Amazon River, by integrating multi-campaign in situ measurements with two decades of MODIS surface reflectance (2001-2024) within a machine learning and Explainable Artificial Intelligence (XAI) framework. Among tested regressors, an optimized Support Vector Regression (SVR_Optuna) consistently outperformed Random Forest, XGBoost, LightGBM, Partial Least Squares, and linear models, achieving R2 = 0.855 in log1p space and RMSE = 12.5 mg m-3 after back-transformation in holdout validation. Applied to MODIS monthly composites, the model reconstructed 24 years of Chl-a variability, revealing recurrent seasonal maxima during receding and low-water phases of the Amazon flood pulse. Spatial analyses highlighted enhanced concentrations in semi-isolated basins and stronger dilution near riverine connections. Pixel-wise uncertainty metrics-derived from temporal variability, bootstrap resampling, and climatological anomalies-indicated robust retrievals in pelagic zones but higher uncertainty along river-lake interfaces. SHAP-based XAI confirmed the dominant role of red and red-edge spectral features (665-709 nm) in Chl-a prediction, consistent with bio-optical theory and strengthening model interpretability. Beyond Curuai, this scalable approach enables systematic monitoring of floodplain lakes across the Amazon Basin, where hydrological connectivity, extreme droughts, and intensifying human pressures increasingly affect water quality.
Climatological variations, triggered by global warming and rising temperatures, have become a growing concern, posing challenges to communities across the United States, Europe, and Asia. Earth heats, climate patterns shift, and the resulting climate instability triggers more persistent and powerful heatwaves, leading to significant ecological and health-related consequences. Disturbances in thermoregulation can lead to elevated core body temperature (CBT>39 °C); this typically occurs during heat stress (HS), a state wherein the body's capacity to cool itself is challenged by multiple external (environmental conditions, pathogens) or internal factors (inflammatory, metabolic, hormonal, and neurological), often precipitating systemic inflammation and multiple organ failure. HS pathological cascade involves different interconnected processes like oxidative stress, inflammation, compromised circulation, disrupted blood-brain barrier (BBB), coagulation irregularities, organ-specific responses, electrolyte imbalances, heat shock proteins (HSPs), and interactions with pre-existing conditions. To effectively address this emerging public health issue, a combined approach is needed, like incorporating pharmacological treatments such as non-steroidal anti-inflammatory drugs (NSAIDs), diuretics, muscle relaxants, vasodilators, beta-blockers, and anti-anxiety agents with essential non-pharmacological supports like public health education, cooling centres, early detection systems, and individualized plans specifically designed for high-risk groups. This review provides insight into the concept of heat-induced injury on the cellular level, the worldwide prevalence of HS, the pathogenic mechanisms behind Heat Stress-induced Multiple Organ Dysfunction (HS-MOD), and the various therapeutic strategies available.
Climate change and air pollution are key determinants of public health, particularly in the onset and exacerbation of respiratory diseases. The main objective is to quantify the lagged and nonlinear effects of climate and air pollution on respiratory hospitalizations in peripheral regions of Costa Rica. This study presents a methodological framework that combines Distributed Lag Nonlinear Models (DLNM) with Generalized Linear Mixed Models (GLMM), incorporating fixed and random effects, to assess the lagged and nonlinear effects of climatic variables and atmospheric pollutants on hospitalizations due to respiratory causes. The response specification was carried out using zero-inflated distributions, aiming to adequately capture the overdispersion and excess zeros present in the data. The analysis focused on peripheral climatological regions and subregions of Costa Rica-territories outside the Central Valley, including Caribbean and Pacific coasts and border areas, characterized by low population density. Weekly data (2000-2019) on temperature, precipitation, relative humidity, and aerosol optical depth (AOD) were combined with seasonal effects and a population offset to account for subregional differences. Northern and Central Pacific regions show similar climate-pollution impacts on respiratory health, while the South Pacific exhibits stronger and more persistent risks from moderate to high pollution, and Atlantic regions show consistently higher risks associated with intense rainfall and high humidity. Overall, precipitation extremes, high humidity, and AOD contribute more to respiratory hospitalizations than temperature. This approach improves explanatory and predictive performance, yields robust relative risk estimates, and captures regional sensitivity to environmental conditions, supporting spatiotemporal health analysis and early warning systems in rural tropical settings.
Emitted from soil as a radioactive noble gas, 222Rn has the potential to be used as a tracer for greenhouse gases. In the atmosphere, it undergoes transport and mixing processes that depend on climatological factors. Therefore, studying factors that influence radon dispersion in hotspot areas can improve our understanding of the driving forces behind atmospheric gas behavior. This study presents data obtained from five years of soil gas measurements at various heights above ground level (18 m, 40 m, and 80 m) below and above the forest canopy in the pristine area of the Amazon Tall Tower Observatory to identify important soil parameters related to radon activity concentration. The results indicate that moisture significantly affects radon activity concentrations during dry and wet periods. The mean 226Ra activity concentration in the soil is 28.3 ± 2.1 Bq/kg. In topsoil, 222Rn varies from 152 to 470 Bq/m3 at ground level, from 1.2 to 10.3 Bq/m3 at 18 m, from 0.37 to 8.6 Bq/m3 at 40 m, and from 1.0 to 5.0 Bq/m3 at 80 m. Daily variations show higher activity concentrations in the early morning, while monthly variations show higher activity concentrations during the dry period.
This study presents a comprehensive spatiotemporal drought assessment for Çanakkale province, Türkiye, utilizing multi-index remote sensing approaches over a 20-year period (2005-2024) coupled with predictive risk modeling for 2025-2027. Four key environmental parameters were derived through the Google Earth Engine platform: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Standardized Precipitation Index (SPI), and Soil Moisture Condition Index (SMCI). Multiple satellite data sources were integrated, including Landsat 7 ETM + , MODIS/MOD11A1, CHIRPS precipitation dataset, and TerraClimate hydrological data. The retrospective analysis revealed significant climatic variability characterized by inter-annual LST fluctuations, progressive NDVI enhancement toward 2024, and pronounced negative trends in both SPI and SMCI indices during recent years. Particularly, SMCI reached - 1.14 in 2023, indicating severe soil moisture deficit. Spatial heterogeneity was evident across the province, with differential vegetation dynamics and precipitation patterns between coastal and interior regions. A Principal Component Analysis-based integrated drought index was developed, explaining 68.7% of total variance, providing comprehensive drought characterization beyond univariate approaches. A hybrid trend-based forecasting framework incorporating seasonal decomposition, climatological constraints, and stochastic variability was implemented. Model validation demonstrated robust performance for LST (R2 = 0.85) and NDVI (R2 = 0.88), while SPI and SMCI exhibited challenges inherent to normalized indices with small-magnitude variations. Prospective projections indicate systematic elevation in composite drought risk from 2.58 (2025) to 2.67 (2026-2027), representing a 3.5% increase and persistent moderate-to-high drought vulnerability. These findings provide critical insights for regional water resource management, agricultural planning, and climate adaptation strategies in Mediterranean ecosystems facing intensifying drought pressures.
Among the myriad consequences of climate change, global warming and extreme weather events are particularly critical due to their well-documented impact on neurological and psychological well-being. However, the severity of these impacts varies significantly by geography. This article assesses the potential adverse effects of climate change on "brain health" through a risk management framework. The analysis begins by evaluating existing governance and risk-assessment procedures, followed by an examination of human adaptive capacities and natural risks. The latter draws upon climatological data-specifically regarding global warming and the "tropicalization" of the Euro-Mediterranean region-as well as anthropological insights. Building on this foundation, we propose strategies for effective risk control, including adaptation, mitigation, and preparedness. Success depends on the mobilization of public health researchers and professionals to drive organizational change and implement preventative measures to address extreme events. Consequently, the article advocates for specific decisions regarding communication, education, and early-warning systems to enhance rescue efficiency and prevent disasters. The discussion concludes with a focus on mitigation strategies specifically tailored to the Euro-Mediterranean region to address the challenges of climate tropicalization.
Fine particulate matter (PM2.5) remains a major challenge for air quality management in South Korea, driven by transboundary transport and domestic emissions. While the Korea-United States Air Quality (KORUS-AQ) campaign offered valuable insights into springtime sources, wintertime pollution has yet to be well characterized. The Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) and Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaigns (February-March 2024) provided a unique opportunity to quantify winter PM2.5 sources using coordinated ground-based, airborne, and satellite observations across East Asia. Here, we present a regional source attribution analysis of surface PM2.5 during the campaign, employing seven chemical transport model configurations and three source attribution approaches, all driven by the updated East Asian anthropogenic emissions inventory (ASIA-AQ v3.0). The model ensemble mean reproduced observed PM2.5 variability at ground sites in China and South Korea with strong correlations (R = 0.79 in China; R = 0.86 in South Korea) and low normalized mean biases (-6.4% and -7.0%, respectively), supporting its robustness for source attribution. Results indicate that continental outflows contributed 57-84% of surface PM2.5 in South Korea, whereas domestic sources accounted for up to 43% under less influence of transboundary transport. The ensemble-based approach provides useful evidence to guide targeted mitigation strategies. These estimates are representative of the specific meteorological conditions during the ASIA-AQ/SIJAQ winter period rather than a climatological mean state.
Understanding precipitation dynamics in arid regions such as Iraq is of paramount importance in hydrological and climatological studies, as it is a key approach to water resources management and climate change adaptation. This study aims to develop a mathematical predictive model for rainfall isotopic values using machine learning techniques. Stable isotope data for oxygen (δ¹⁸O) and deuterium (δ²H) in precipitation were collected from 32 meteorological stations distributed across Iraq over a 14-year period (2010–2024). The dataset also included meteorological parameters for these stations, including precipitation amount, air temperature, relative humidity, and calculated station elevation. Several machine learning algorithms (i.e., SVM, GBR, ANN, CatBoost, XGBoost, and RF) were employed to compare predicted isotopic values with actual readings, accounting for rainfall characteristics and patterns. The results demonstrated that the RF model achieved superior predictive performance, with a calibration coefficient (R²) of 0.89 in the testing set, indicating strong predictive capability. This model also recorded the lowest mean absolute error (MAE) of 1.39 and the lowest root mean square error (RMSE) of 3.5 compared to the other algorithms, reflecting improved predictive accuracy. These findings confirm the effectiveness of integrating machine learning, particularly the RF approach, in enhancing the modeling of isotopic signature predictions in environmental studies. Furthermore, they highlight the potential of AI-based models as powerful tools for reconstructing historical isotopic datasets, supporting climate variability assessment and sustainable water resources management in arid and semi-arid regions.
Exposure to ultraviolet (UV) radiation significantly impacts human health. Consequently, comprehensive UV climatological databases are of great interest. UV exposure is evaluated by weighting UV spectra with spectral functions that describe physiological responses at each wavelength. The most widely used function is the erythemal weighting function, which is used to compute the UV Index (UVI) to assess the health risk associated with UV overexposure. The ERA5 datasets, produced by the Copernicus Climate Change Service (CDS), offer hourly ground-level UV radiation ([Formula: see text]), but do not include UVI. This study proposes a model to compute hourly UVI using exclusively ERA5 data, enabling direct access through the CDS to derive UVI statistics for locations of interest and potentially supporting the integration of a dedicated UVI product into ERA5. The model was developed using UV spectra simulated under clear-sky conditions with the uvspec radiative transfer model, accounting for atmosphere type, solar zenith angle, visibility, altitude, albedo, total ozone, and aerosol type. For these parameters, representative values typical of tropical, mid-latitude, and subarctic regions were used, effectively excluding Arctic conditions, and considering UVI values ≤ 12. The resulting formulation expresses UVI as a function of [Formula: see text], sun elevation, and total ozone. The model was validated using ground-based UVI measurements from six stations (over 17000 cases) and, in addition, compared with UVI derived from Copernicus Atmospheric Monitoring Service (CAMS) products (over 6000 cases) taken as a reference. Performance was assessed through the statistics of the differences between measured/modelled values and CAMS data under three scenarios: clear-sky conditions, varying cloud cover, and all-sky conditions. Under clear-sky conditions, the model uncertainties showed a small positive bias (≤ 0.5), with the absolute difference (AD) < 1 in 73% of the cases for ground measurements and in 86% of the cases for CAMS. The root mean squared difference (RMS) and the mean absolute deviation (MAD) were 0.9 and 0.7, respectively, for ground measurements, and 0.7 and 0.6 for CAMS. Under cloudy-sky conditions, model performance worsens significantly for CC > 0.4, with RMS and MAD reaching values of about 1.5. However, when considering relative uncertainties (percentage ratio between RMS and reference values of UVI), up to CC < 0.7 the RMS% remains below 15% for Very High-to-Extreme WHO/ICNIRP exposure categories and below 20% for Moderate-to-Extreme categories. A comparison between CAMS UVI and ground measurements was also performed, yielding results consistent with those described above. As an example, Appendix A illustrates how the model can be applied to generate daily and monthly UVI statistics over large geographical areas using only ERA5 data accessed through the CDS web portal.
Increases in area of extent, severity, and frequency of wildfires across the western United States are presenting challenges to socio-ecological systems, including shifts to alternative ecological states, loss of homes, and compromising human health. Wildfire suppression operations, such as constructing hand lines to limit the spread of fire, are an important part of wildland fire management, particularly in the wildland urban interface. Like other attributes of wildland fire activity and effects, suppression strategies and their effectiveness vary with ecological, topographic, climatological, and sociopolitical factors. However, there has been little research that examines the efficacy of suppression operations, specifically as they relate to forest composition. Here, we ask about the effectiveness of fire line construction based on adjacent stand composition. Specifically, we ask: (1) Are wildfire suppression lines preferentially constructed in stands with specific tree species? (2) How does species identity influence the probability that suppression lines hold when also considering differences in topography, climate, and extreme fire weather? We anticipated that suppression operations will be biased towards-and more effective in-stands with quaking aspen because they are often associated with less extreme fire behavior than many conifer species. We conducted our study in the southern Rocky Mountain ecoregion using fires (n = 36) that burned during 2019-2023 and included records of fire suppression operations (n = 4295). We used nonparametric statistical models to elucidate biases in the construction of fire lines and the effects of stand composition. We found quaking aspen was the least common tree species to be within fire footprints, yet fire lines were placed near quaking aspen 1.68-5.30 times more than commonly co-occurring tree species. Fire growth, independent of stand composition, was the most important predictor for whether fire suppression lines were likely to hold but the percentage of fire lines that held differed between fires >40,500 ha and smaller events (65% vs. 82%, respectively). This research suggests that wildland firefighters preferentially located fire lines near aspen stands, perhaps due to the long-held notion that aspen stands are less flammable. However, during extreme burning conditions fire lines are unlikely to hold regardless of stand composition.