Development Of Warning System Research Articles

The Impact of Climate Variability on Cattle Heat Stress in Vanuatu

Heat stress is a climate extreme that impacts cattle health, fertility, feed intake, production, and well-being. In Vanuatu, the beef industry is crucial to local livelihoods and the nation’s economy, thus the objective of this study was to examine the impact of heat stress on cattle health and production. This study uses the Heat Load Index (HLI) and Accumulated Heat Load (AHL) as proxies to assess the impact of heat stress on cattle in Vanuatu over a 30-year period (1994–2023), using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data. The analysis examines historical patterns of heat stress in cattle across Vanuatu, identifying more instances of heat stress occurring during the wet season due to characteristically elevated temperatures, humidity, and low wind speeds. Findings also suggest that El Niño events may increase the intensity and duration of heat stress events. These insights inform the development of an Early Warning System for heat stress in cattle, establishing a crucial foundation for targeted adaptation strategies aimed at enhancing the resilience and sustainability of Vanuatu’s beef industry to climate variability and change.

Early warning and dynamics of compound rockslides: lessons learnt from the Brienz/Brinzauls 2023 rockslope failure

AbstractPrediction of rockslope dynamic evolution and catastrophic failure remains a fundamental challenge for scientists and practitioners, even though this topic has been studied for decades. Here we report about a case in the Swiss Alps, which has received worldwide attention, not only because a village was evacuated on May 12, 2023, and traffic corridors were progressively closed prior to the slope collapse, but also because this site had been studied and monitored with unprecedented detail during many years before failure. In 2023 the structural and dynamic evolution of the 2 million m3 suspended compound rockslide at Brienz/Brinzauls was closely monitored with diverse ground-based systems and continuously modeled with Voight’s creep law. Substantial deviations from this classical failure model were observed, which are in most cases not related to variations in external driving factors, but diverse internal strength degradation processes. This led to a systematic analysis of Voight’s model uncertainty and the development of an early warning system which includes supplementary early warning parameters to the classical velocity thresholds. We show how to treat the spatial and temporal variations of velocity thresholds and when they reach their applicability limit in a real-time early warning model. The unstable compartment at Brienz/Brinzauls progressively collapsed in the night of June 15, 2023, and generated a series of dry granular flows and rock mass falls, which just stopped at the Brienz settlement boundary.

Updates to the wild bird abundance and movement models for the early warning system for avian influenza in the EU

Abstract Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

Lake responses and mechanisms to El Niño on the Tibetan Plateau using deep learning-based semantic segmentation

Numerous lakes across the Tibetan Plateau (TP) serve as crucial indicators of climate change and are significantly influenced by El Niño events. Previous studies of lake response to El Niño events have focused on a limited number of lakes. Despite advances in remote sensing technology, there have been few comprehensive and large-scale studies using deep learning, and there are still gaps in understanding the response mechanisms on a larger scale. This study leverages advanced deep-learning techniques to map lake responses, offering unprecedented insights into the large-scale hydrological impacts of El Niño. Our results show that lakes shrink significantly during El Niño events on the TP. Lakes located in the central and southern parts of the TP and small lakes with areas ranging from 1 to 50 km2 (over 60 % of them) exhibited strong responses. The range of lake response to El Niño events varies with their intensity, with stronger El Niño events causing an expansion of the response range along the latitudinal direction. We propose four possible mechanisms for lake response patterns to El Niño from the perspective of lake water sources. Strong shrinkage is primarily caused by decreased precipitation and increased evaporation, with a possible contribution from reduced meltwater. Strong expansion is due to increased precipitation, more glacier and frozen soil meltwater, and reduced evaporation. For slight shrinkage and expansion patterns, the balance of meltwater may offset or even counteract the El Niño signal. The study’s results could improve predictions of extreme weather events like droughts and floods in the Third Pole region, enhance water resource management and responsiveness, and offer valuable insights for ecological monitoring and early warning systems development.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

Enzyme-Mediated Duplex Exponential Amplification: A New Platform for Rapid Screening of <i>Hylurgus ligniperda</i>

As the world's second largest timber importer, wood demand in China has been growing extremely rapidly, leading to an increase of 163% from 2009 to 2018. The plant quarantine pest <i>H. ligniperda Fabricius, 1787</i> is an invasive species frequently intercepted at ports. <i>H. ligniperda</i> causes damage mainly to pine and spruce. To improve the efficiency of on-site inspection and the efficacy of early detection, tight quarantine in ports, time-effective identification, and a national surveillance program for high-risk invasive bark beetles are in urgent need. In this study, a simple, fast and accurate classification method for <i>H. ligniperda </i>is established based on the enzyme-mediated duplex exponential amplification (EmDEA) technique. Partial region from <i>inhibitor of apoptosis 2</i> (<I>IAP2)</I> gene was selected as the target and 6 primer/probe combinations were designed. Through selection, the combination of 3-HY-F3, 3-HY-R2 and 3-HY-RNA5 was chosen as the final primer-probe set, as it showed the lowest Ct with highest final fluorescence signal. Method validation and specificity test using 6 other beetle species living on coniferous wood showed that this method is result reliable and specific. Through parameter analysis with positive plasmid, the detection limit was calculated to be 13.6 copies/μL (9×10<sup>-7</sup> ng DNA/ reaction), much higher than conventional molecular methods such as PCR. The whole process including isothermal amplification, data analysis, and result output can be finished in 30 min, which is highly time-effective. Besides, the operation is simple and little training is needed for non-professionals. The application prospects of this rapid screening system include customs screening in ports, wild survey in non-lab situations and early warning system development. The new analysis platform EmDEA, can also be implemented in rapid detection and identification of other forestry pests.

Do Climate Shocks Disadvantage Household Investment in Human Capital in Benin? An Approach Based on the Endogenous Treatment Regression Model

The increasing number of drought and flood shocks in Benin is causing considerable economic losses and social disruption. This article looks specifically at the effects of these shocks on household human capital expenditure in three climatic zones of Benin to highlight the effects specific to each zone. The data used come from the Harmonized Survey of Household Living Conditions. A linear regression model of the endogenous treatment was used for the analyses. The findings indicate that climate shocks reduce household capital expenditure and that this impact depends on the household’s climatic zone of residence. Investment in resilient infrastructure, such as water drainage and anti-flood systems, is suggested. The implementation of social and health assistance measures and school subsidies to cushion the impact of climatic shocks on the human capital expenditure of disaster-stricken households. The development and maintenance of an early warning system to anticipate the occurrence of shocks, floods, and droughts would also contribute to mitigation actions.

Insights from a topo-bathymetric and oceanographic dataset for coastal flooding studies: the French Flooding Prevention Action Program of Saint-Malo

Abstract. The French Flooding Prevention Action Program of Saint-Malo, France, requires the assessment of coastal flooding risks and the development of a local flood warning system. The first prerequisite is knowledge of the topography and bathymetry of the bay of Saint-Malo; the acquisition of new multibeam bathymetric data was performed in 2018 and 2019 to increase the resolution of the existing topo-bathymetric datasets and to produce two high-resolution (20 and 5 m) topo-bathymetric digital terrain models. Second, the hydrodynamics associated with coastal flooding were investigated through a dense and extensive oceanographic field experiment conducted during winter 2018–2019 using a network of 22 moorings with 37 sensors: the network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers, and 4 acoustic wave current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset thus provides an extended overview of the hydrodynamics and wave processes in the bay of Saint-Malo from the coast up to over-flooding and over-topping areas. This dataset helps to identify the physical drivers of the coastal flooding and provides a quantification of their respective contributions. In particular, the wave processes at the foot of the protection structures can be observed: in this macro-tidal environment, during high spring tides, short and infragravity waves propagate up to the protection structures, while the wave set-up remains negligible, and over-topping by sea packs can occur. The combination of high-resolution topo-bathymetric and oceanographic datasets allows the construction, calibration and validation of a wave and hydrodynamic coupled model that is used to investigate flooding processes more deeply and might be integrated into a future local warning system by means of Saint-Malo inter-communality. The topo-bathymetric and oceanographic datasets are available freely at https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84 and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO (Shom, 2020a, b, 2021).

Skilful probabilistic medium‐range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system

AbstractDengue fever is a source of substantial health burden in Vietnam. Given the well‐established influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a world‐leading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (May–October) ensemble precipitation and 2‐m temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001–2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce district‐level epidemiological forecasts for urban areas where dengue is most prevalent.

Early warning systems for financial distress: A machine learning approach to corporate risk mitigation

This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.

Strategies for Combating Adversarial Information Operations: Theory and Practical Applications

In the contemporary information landscape, the proliferation of disinformation and propaganda poses a significant challenge to societal discourse and democratic processes. This paper proposes a multi-disciplinary approach to combatting adversarial information operations, drawing upon theoretical frameworks and practical applications. Theoretical foundations are established through an examination of the Persuasive System Design (PSD) model (Oinas-Kukkonen, 2013) and its parallels with propaganda tactics. By analyzing the shared flaws and vulnerabilities, insights emerge into the manipulation techniques employed by threat actors in online information spaces. Building upon this theoretical framework, the paper presents a proactive strategy for countering disinformation: the development of Early Warning and Control Systems (EWACS). These systems leverage AI-assisted narrative discovery to monitor the digital information landscape continuously. By identifying emerging threats and inauthentic activity, strategic communicators gain valuable insights for crafting counter-narratives and pre-emptive communication strategies. Key components of the proposed approach include deterrence by denial and resilience-building measures. By shifting the cost-gain calculation of adversaries and enhancing societal resilience, the aim is to create an environment where propagandists face increased challenges in achieving their objectives. This paper emphasizes the importance of collaboration between diverse stakeholders, including governmental organizations, academia, NGOs, and journalists. By harnessing the collective expertise from multiple fields, more effective strategies can be developed to safeguard information integrity and restore public trust. In conclusion, this paper advocates for a convergence of theory and practice in addressing the complex challenges posed by adversarial information operations. By integrating theoretical insights with practical applications, the proposed approach offers a holistic framework for countering disinformation and propaganda in contemporary information environments.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.

Development of an early warning system for higher education institutions by predicting first‐year student academic performance

AbstractIn this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.

Composition of factors for local heat adaptation measures at the local level in cities of the mid-latitude – An approach for the south-west of Germany

Traditional heat health warning systems focus on severe and extreme heat events at the district or regional level, often overlooking localized risk and protective factors such as healthcare access and urban green spaces. This approach considers less the varying impacts of heat within cities, including the phenomenon of Urban Heat Islands (UHIs) and the diverse needs of different populations. To address these shortcomings, a need for the development of an Urban Heat Health Warning and Information System (UHHWIS) that operates within the framework of Heat Health Action Plans is needed. Such a system integrates national acute heat health warnings with city-specific assessments of UHI effects and other relevant factors. The technical implementation of the UHHWIS involves the calculation and preprocessing of basic factors such as the Normalised Difference Vegetation Index (NDVI), imperviousness, and UHI intensity. Additionally, further factors are assessed, spatially processed, and provided in accordance with Open Geospatial Consortium (OGC) standards. An iso-area analysis is conducted to evaluate the accessibility of protective factors, such as urban green spaces, drinking wells, hospitals, physicians, and pharmacies, based on the city’s road topology. One crucial factor considered in the system is the casting of shadows, which is influenced by both time and location and facilitated through deck.gl. The developed template encompasses all these components into a unified system aimed at protecting vulnerable and risk groups, such as the elderly, through resilient, climate-adapted urban planning. The system provides warnings and information tailored to the urban morphology and prevailing conditions, complemented by a catalogue of potential short- to long-term measures focused on behavioral changes and climate-resilient urban planning strategies. The template can be adapted for use in various European cities, offering valuable insights to decision-makers in city administration for mitigating thermal stress and enhancing resilience against urban heat nowadays and in future.

Development of Athlete Health Monitoring and Early Warning System Based on Sensor Data

An athlete health monitoring and early warning system based on sensor data utilizes wearable sensors to track various physiological parameters, such as heart rate, body temperature, oxygen saturation, and movement patterns. These sensors continuously collect real-time data during training sessions and competitions. Machine learning algorithms analyze the data to detect deviations from normal patterns and identify potential indicators of injury, fatigue, or overtraining. By monitoring athletes' health parameters over time and comparing them to established baseline values, the system can provide early warnings of potential health issues or performance declines. Coaches and medical staff can then intervene promptly with appropriate interventions, adjustments to training loads, or rest periods to prevent injuries and optimize athletes' performance and well-being. . The paper introduces the Reliable Fuzzy Sensor Optimized Automated Monitoring (RFSO-DL) system, a novel approach to athlete health monitoring leveraging fuzzy logic and sensor data optimization techniques. RFSO-DL offers a comprehensive solution for real-time assessment of athlete well-being by analyzing physiological measurements and activity levels. Through a series of experiments, the system demonstrates high accuracy, precision, recall, and F1 score in classifying athlete health status. Results indicate RFSO-DL's effectiveness in capturing subtle variations in health status and providing timely interventions. Through experiments, the system achieves impressive results, with accuracy ranging from 90.5% to 94.1%, precision from 85.8% to 91.2%, recall from 91.7% to 95.6%, and F1 score from 88.6% to 93.3%. These findings underscore RFSO-DL's potential to revolutionize athlete health monitoring by accurately classifying health status based on physiological measurements and activity levels.

Development of an Early Warning System Using Social Media for Flood Disaster

Development of an Early Warning System Using Social Media for Flood Disaster

Early Warning System and Crisis Management

This research was made to find out the development map on the research topic with the theme Early Warning Economic Crisis. This study discusses bibliometric mapping of the development of trends in keywords, authors, and journals on digital banking from a number of Scopus-indexed reputable papers published between 1990 and 2021. Early warning systems help organizations reduce losses and are critical in detecting emergencies from occurring. This research contributes to the field of early warning system development and applications in the economic and financial sectors by providing new knowledge and critical insights into a previously unexplored topic. Our research also contributes to a better understanding of the use of Early warning systems in business, finance, and economics. Consequently, accumulating knowledge to detect risks before it is too late is crucial for companies to maintain a competitive advantage in the market.

Estimation of Intensity‐Duration‐Area‐Frequency Relationships Based on the Full Range of Non‐Zero Precipitation From Radar‐Reanalysis Data

AbstractIntensity‐Duration‐Area‐Frequency (IDAF) models provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They play a critical role in hydrological design, areal rainfall hazard quantification, storm characterization, and early warning system development. IDAF models extend the conventional Intensity‐Duration‐Frequency models by accounting for the spatial extent of precipitation (i.e., the area). In this study, we develop IDAF models using the entire non‐zero precipitation intensities, not only the extremes. We use the extended generalized Pareto distribution (EGPD) to model the precipitation intensities. To build the IDAF models, we adopt a data‐driven approach that allows the linkage of EGPD parameters with duration and area, based on empirically determined parametric relationships. The inference of model parameters is done using a global maximum likelihood estimation, and uncertainties are assessed by the bootstrap method. The study area is Switzerland, a topographically complex region of 42,000 km2 with regional precipitation variability and clear seasonality. The study utilizes 17 years of data from CombiPrecip, a radar‐reanalysis product developed by geostatistically merging radar and rain gauge data in an operational setting. We build the IDAF models for the spatiotemporal range of 1–72 hr and 1 to 1,089 km2 at each pixel in the study area. To the best of our knowledge, our study is the first attempt to use the EGPD in IDAF curve modeling. It discusses the use and limitations of CombiPrecip in extreme value analysis and highlights the challenges of modeling areal precipitation in a complex topographical environment.

Developing an Australian dust early warning system

In Australia, the perception of large continental-scale dust storms has traditionally been restricted to aspects of ‘inconvenience’ or ‘novelty’. However, as the climate changes, there is an increased likelihood of dust storm activity that puts communities at increased risk. The aim of this study was to explore the need and possibility of developing a dust early warning system for Australia. Through a scoping review of internationally used sand and dust early warning systems, we found that an array of systems exist or were theoretically tested in the Northern Hemisphere. The sensor networks, one of which was operational, were of particular interest because Australia already has an operational air quality network that could be expanded to a dust early warning system. The need for a trans-boundary system is critical for Australia and, therefore, requires a combined approach of expanded sensor network with satellite-based systems to increase validation of any future modelling approach. This improved understanding could inform the development of a dust warning system for multi-socio-economic factors that account for cumulative exposure to small, localised and large continental-sized dust events.