Search Results (169)

Despite a growing number of Earth Observation satellites, a critical observational gap persists for timely, high-resolution flood mapping, primarily due to infrequent satellite revisits and persistent cloud cover. To address this issue, we propose a novel framework that synergistically fuses complementary data from three public sensor types. Our methodology harmonizes these disparate data sources by using surface water fraction as a common variable and downscaling them with flood susceptibility and topography information. This allows for the integration of sub-daily observations from the Visible Infrared Imaging Radiometer Suite and the Advanced Himawari Imager with the cloud-penetrating capabilities of the Advanced Microwave Scanning Radiometer 2. We evaluated this approach on the February 2022 flood in Brisbane, Australia using an independent ground truth dataset. The framework successfully compensates for the limitations of individual sensors, enabling the consistent generation of detailed, high-resolution flood maps. The proposed method outperformed the flood extent derived from commercial high-resolution optical imagery, scoring 77% higher than the Copernicus Emergency Management Service (CEMS) map in the Critical Success Index. Furthermore, the True Positive Rate was twice as high as the CEMS map, confirming that the proposed method successfully overcame the cloud cover issue. This approach provides valuable, actionable insights into inundation dynamics, particularly when other public data sources are unavailable. Full article

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values. Full article

This study investigates the influence of different effective precipitation (P_e) estimation methods on drought index performance in a humid region of Türkiye. The standard precipitation index (SPI) and the reconnaissance drought index (RDI) were compared with their effective precipitation-based counterparts, Agricultural Standardized Precipitation Index (aSPI) and Effective Reconnaissance Drought Index (eRDI), using four P_e estimation methods: USBR (U.S. Bureau of Reclamation), USDA-(Simplified and CROPWAT) (U.S. Department of Agriculture), and FAO (Food and Agriculture Organization). Data from three closely located meteorological stations (Çanakkale, Bozcaada, and Gökçeada) were analyzed across multiple time scales (1-, 3-, 6-, 12-month, and annual). Statistical metrics—coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE)—were used to assess the indices, and trend analyses were conducted using the Mann–Kendall and Sen’s Slope tests. The USDA-Simplified method consistently showed the highest accuracy across all stations and time scales (R² ≈ 0.99; lowest RMSE ≈ 0.09; NSE > 0.95), while the FAO method performed poorly, particularly at the 1-month scale. Drought frequency and severity were found to increase with time scale, contrary to trends observed in arid regions. Trend analysis revealed no significant changes at short time scales, but statistically significantly increasing drought severity was detected in longer scales, especially in Çanakkale, with slopes reaching up to –0.018 per year. The findings highlight the importance of selecting appropriate P_e estimation methods for accurate drought assessment, even in humid climates, and support the use of aSPI and eRDI with the USDA-Simplified method. Full article

Climate change is increasing the frequency and intensity of Marine Heatwave (MHW) events, threatening Western Tropical Pacific Small Island Developing States (SIDSs). MHWs critically threaten the fisheries sector which vitally supports food and nutrition security in local communities and local livelihoods. Currently, MHW risk to fisheries in Western Tropical Pacific SIDSs remains underexplored. Vanuatu is a Western Tropical Pacific SIDS which requires expanded MHW risk knowledge to improve the adaptive capacity of fisheries. A fundamental method for expanding MHW risk knowledge is tailored risk assessment. This study conducts the initial steps in a tailored MHW risk assessment methodology, displaying how a tailored indicator selection and weighting process can inform effective MHW risk assessment for fisheries in Western Tropical Pacific SIDSs. Hazard, vulnerability, and exposure indicators were selected through a combined process utilising a literature review and participatory research survey. Survey results were also used to develop a user-informed indicator weighting scheme. Selected indicators included sea surface temperature (SST), coral bleaching/mortality, and chlorophyll-a concentration (hazard); terrestrial-based food and income generation, fishing skills and technology, fishery fish diversity/fishery flexibility, and primary production of commercial fisheries (vulnerability); seagrass population/C content, coral habitat health/crown-of-thorns prevalence, crab stock health, and fish mortality/fish stock health (exposure). These indicators and their assigned weights are recommended for use in a future MHW risk assessment for Vanuatu fisheries. A tailored, fisheries-specific MHW risk assessment could advise local decision-makers on where/when MHW risk is high and aid the implementation of more effective fisheries risk management. Full article

Australia’s renewable energy transition will be dominated by solar and wind power, yet their contrasting variability necessitates hybrid integration with storage to ensure reliability. This study uses Australian reanalysis data, BARRA (Bureau of Meteorology Atmospheric High-Resolution Regional Reanalysis for Australia), to quantify solar (global horizontal irradiance, GHI) and wind (wind power density, WPD) resources by examining their availability, variability, synergy, episode length, and lulls. The novelty of this work is the use of rarely examined metrics such as variability, availability, episode length, and extended lull events (Dunkelflaute) with a high-resolution and 29-year duration reanalysis dataset. The results show that solar is the more reliable resource, with high daytime availability and relatively short lulls. Wind, despite being abundant in coastal regions, is highly intermittent, characterized by a skewed distribution, low availability, and extended periods of lulls. Synergy metrics demonstrate significant complementarity, with combined solar–wind synergy reducing deficits in single resources, while joint non-synergy events define critical system vulnerabilities. Importantly, hybrid systems limit maximum joint lulls, which are far shorter than wind-only extremes, thereby reducing the scale of long-duration storage required. These findings underscore that, while solar provides a stable baseline supply and wind contributes spatial diversity, hybrid systems supported by batteries offer a resilient pathway. Synergy and non-synergy statistics provide essential parameters for optimally sizing storage to withstand rare but severe shortfalls, ensuring a reliable, utility-scale renewable future for Australia. Full article

Wetlands are an important part of coastal ecosystems but are under increasing pressure from climate change-induced sea-level rise and flooding, in addition to development pressures associated with increasing human populations. The change in tidal events and their intensity due to sea-level rise is also reshaping and challenging the vitality of existing wetland systems, requiring more intensive localized studies to identify future-focused restoration and conservation strategies. To support this endeavor, this study utilizes tide gauge datasets from the Australian Bureau of Meteorology (BOM) for maximum sea-level (Hmax) prediction and Landsat Collection surface reflectance datasets obtained from the United States Geological Survey (USGS) database to detect and project patterns of change in the Maroochy River floodplain of Queensland, Australia. This study developed an efficient hybrid deep learning model combining a Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNNBiLSTM) architecture for the prediction of maximum sea-level and tidal events. The proposed model significantly outperformed three benchmark models (Multiple Linear Regression (MLR), Support Vector Regression (SVR), and CatBoost) in achieving a high correlation coefficient (r = 0.9748) for maximum sea-level prediction. To further address the increasing frequency and intensity of tidal events linked to sea-level rise, a CNNBiLSTM classification model was also developed, achieving 96.72% accuracy in predicting extreme tidal occurrences. This study identified a significant positive linear increase in sea-level rise of 0.016 m/year between 2014 and 2024. Wetland change detection using Landsat imagery along the Maroochy River floodplain also identified a substantial vegetation loss of 395.64 hectares from 2009 to 2023. These findings highlight the strong potential of integrating deep learning and remote sensing for improved prediction and assessment of sea-level extremes and coastal ecosystem changes. The study outcomes provide valuable insights for informing not only conservation and restoration activities but also for providing localized projections of future change necessary for the progression of effective climate adaptation and mitigation strategies. Full article

Drought is a complex and impactful natural hazard, with sometimes catastrophic impacts on small or subsistence agriculture and water security. In Pacific Island countries, there lacks an agreed approach for monitoring agricultural drought hazard with satellite-derived remote sensing data. This study addresses this gap through a framework for agricultural drought monitoring in the Pacific using freely available space-based observations. Applying World Meteorological Organization’s (WMO) recommendations and a set of objective selection criteria, three remotely sensed drought indicators were chosen and combined using fuzzy logic to form a composite drought hazard index: the Standardised Precipitation Index, Soil Water Index, and Normalised Difference Vegetation Index. Each indicator represents a subsequential flow-on effect of drought on agriculture. The index classes geographic areas as low, medium, high, or very high levels of drought hazard. To test the drought hazard index, two case studies for drought in the western Pacific, Papua New Guinea (PNG), and Vanuatu, are assessed for the 2015–2016 El Niño-related drought. Findings showed that at the height of the drought in October 2015, 58% of PNG and 72% of Vanuatu showed very high drought hazard, compared to 6% and 40%, respectively, at the beginning of the drought. The hazard levels calculated were consistent with conditions observed and events that were reported during the emergency drought period. Application of this framework to operational drought monitoring will promote adaptive capacity and improve resilience to future droughts for Pacific communities. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Asheville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Asheville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United States from Helene. This is of extreme concern and should be investigated in detail as the public expects the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, runoff from such rainfall events increases, which causes greater devastation. Full article

Thunderstorms downbursts can be hazardous during aircraft landing and take-off. A warming climate increases low- to mid-level troposphere water vapor, typically transported from high sea-surface temperature regions. Consequently, the future occurrence and intensity of destructive wind gusts from wet microburst thunderstorms are expected to increase. Wet microbursts are downdrafts from heavily precipitating thunderstorms and are several kilometers in diameter, often producing near-surface extreme wind gusts. Brisbane airport recorded a wet microburst wind gust of 157 km/h in November 2016. Numerous locations in eastern Australia experience warm season (October to March) wet microbursts. Here, eight machine learning techniques comprising forward and backward linear regression, radial basis forward and backward support vector regression, polynomial-based forward and backward support vector regression, and forward and backward random forest selection were employed. They identified primary attributes for increased atmospheric instability by warm moist air influx from regions of high sea-surface temperatures. The climate drivers detected here are indicative of increased future eastern Australian warm season thunderstorm downbursts, occurring as wet microbursts. They suggest a greater frequency and intensity of impacts on aircraft safety and operations affecting major east coast airports, such as Sydney and Brisbane, and smaller aircraft at inland regional airports in southeastern Australia. Full article

In the South Pacific, an increase in the frequency of climate hazards has resulted in worsened human and animal health outcomes, revealing the need for strengthened early warning to increase hazard preparedness. As Vanuatu is one of the most at-risk countries to natural disasters, an early warning system (EWS) for climate hazards is essential to support industries and communities. Notably, climate variability has been found to exacerbate communicable disease burden and compromise livestock health and productivity; however, forecasting of such hazards and their compounding effects has not been developed in Vanuatu. Therefore, our study aims to explore EWSs that monitor and predict the impact of climate variables on malaria incidence and cattle heat stress in Vanuatu. Using monthly precipitation and temperature, a Bayesian model was developed to predict provincial malaria case burden in Vanuatu. Additionally, this study developed a weekly forecasting model to predict periods of cattle heat stress. This model used the Heat Load Index (HLI) as a proxy for heat stress to identify periods of increased heat load and antecedent conditions for cattle heat stress across the provinces. This study was successful in establishing proof-of-concept risk forecasts during selected case study periods: January 2020 and January 2016 for malaria transmission and cattle heat stress, respectively. To contribute towards a future multi-hazard EWS framework for climate hazards in Vanuatu, bulletins for predicted climate-based malaria transmission and cattle heat stress risk were developed to inform key decision makers. Intended to enhance preparedness for malaria outbreaks and cattle heat stress events, this study’s exploration of EWSs can support the resilience of Vanuatu’s public health and agricultural sectors in the face of escalating climate challenges. Full article

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period, from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

Malaria, a climate-sensitive mosquito-borne disease, is widespread in tropical and subtropical regions, and its elimination is a global health priority. Malaria is endemic to Vanuatu, where elimination campaigns have been implemented with varied success. In this study, climate variables were assessed for their correlation with national malaria cases from 2014 to 2023 and used to develop a proof-of-concept model for estimating malaria incidence in Vanuatu. Maximum, minimum, and median temperatures; diurnal temperature variation; median temperature during the 18:00–21:00 mosquito biting period (VUT); median humidity; and precipitation (total and anomaly) were evaluated as predictors at different time lags. It was found that maximum temperature had the strongest correlation with malaria cases and produced the best-performing linear regression model, where malaria cases increased by approximately 43 cases for every degree (°C) increase in monthly maximum temperature. This aligns with similar findings from climate–malaria studies in the Southwest Pacific, where temperature tends to stimulate the development of both Anopheles farauti and Plasmodium vivax, increasing transmission probability. A Bayesian model using maximum temperature and total precipitation at a two-month time lag was more effective in predicting malaria incidence than using maximum temperature or precipitation alone. A Bayesian approach was preferred due to its flexibility with varied data types and prior information about malaria dynamics. This model for predicting malaria incidence in Vanuatu can be adapted to smaller regions or other malaria-affected areas, supporting malaria early warning and preparedness for climate-related health challenges. Full article

Temperature is a critical environmental factor affecting the growth and development of rice, especially under the backdrop of global climate change, where temperature fluctuations have become increasingly significant in influencing the growth cycle and development rate of rice. To comprehensively assess the impact of temperature variations on the different growth stages of rice, this study integrates data from multiple relevant studies published between 1980 and 2024. By selecting research focused on the influence of temperature changes on the rice development cycle, a meta-analysis is conducted to systematically evaluate the effects of temperature on the growth rates of rice during its six key developmental stages. The results indicate that increased temperatures significantly accelerate the development rate of rice during all growth stages, with a general acceleration in growth speed at different developmental phases. The study further found that when the temperature ranges from 28 °C to 32 °C, the growing conditions for rice are most favorable, exhibiting the optimal development rate. This study provides scientific evidence for understanding how temperature changes affect rice growth and development and offers valuable references for rice cultivation management and climate adaptation strategies. Full article