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Early warning systems play significant roles in disaster risk reduction and management. However, a global picture of how they function on the ground, especially in developing countries, is lacking. This study assessed social innovations and drivers in the community‐based transboundary flood early warning systems in the Ugandan context. The study found that the community‐based transboundary flood early warning system generated three social innovations: new inter‐community relations, new community‐local resource relations, and new housing and bedding structures. New inter‐community relations were driven by the transboundary nature of the flood and kinship. New community‐local resource relations were driven by the lack of government support for the early warning system. New housing and bedding structures were driven by the uncertainty about the flood at night. The study confirms the importance of social market failure in driving social innovations and the role of community‐based flood early warning systems in promoting the utilisation of local resources. The effectiveness of transboundary early warning systems in extending lead time and reducing losses was also confirmed. However, the early warning system was found to be effective only during day time. The study, therefore, recommends government intervention in bridging the early warning system gap by installing telemetry.

This paper assessed existing EWS challenges and opportunities in cloud computing through the PSALSAR framework for systematic literature review and meta-analysis. The research used extant literature from Scopus and Web of Science, where a total of 2516 pieces of literature were extracted between 2004 and 2022, and through inclusion and exclusion criteria, the total was reduced to 98 for this systematic review. This review highlights the challenges and opportunities in transferring in-house early warning systems (that is, non-cloud) to the cloud computing infrastructure. The different techniques or approaches used in different kinds of EWSs to facilitate climate-related data processing and analytics were also highlighted. The findings indicate that very few EWSs (for example, flood, drought, etc.) utilize the cloud computing infrastructure. Many EWSs are not leveraging the capability of cloud computing but instead using online application systems that are not cloud-based. Secondly, a few EWSs have harnessed the computational techniques and tools available on a single platform for data processing. Thirdly, EWSs combine more than one fundamental tenet of the EWS framework to provide a holistic warning system. The findings suggest that reaching a global usage of climate-related EWS may be challenged if EWSs are not redesigned to fit the cloud computing service infrastructure.

Flood events are the most common weather‐related hazard in Europe and Spain, comprising 41% of such events between 2001 and 2020. Mediterranean catchments, with steep slopes and short river courses, are particularly vulnerable to intense convective rainfall, often triggering flash floods. To address this risk, the University of the Balearic Islands developed RiscBal, an innovation ecosystem featuring a high‐resolution Multi‐Hazard Early Warning System. Its core, RiscBal‐Warnings, integrates real‐time data from 56 discharge‐monitoring stations and 32 rainfall/soil moisture stations, forming the RiscBal‐Control network. These stations are positioned in high‐risk and historically flood‐prone areas. This paper focuses on the innovation management behind RiscBal's design and integration into regional governance. Through interdisciplinary collaboration, stakeholder co‐creation, and institutional alignment, RiscBal demonstrates how managing innovation can translate scientific knowledge into actionable, context‐sensitive solutions. The system's performance was tested during the August 15, 2024, flash flood in Es Mercadal, Menorca, providing critical lead time for emergency response. However, issues like telecommunication gaps and early‐stage hydrological modeling prompted improvements, including redundant systems. Riscbal's modular and interoperable design supports polycentric risk governance and continuous feedback between academia, government, and municipalities. Adaptable to other Mediterranean and global flood‐prone regions, it offers a replicable framework for climate resilience. The paper also explores adoption challenges, emphasizing trust, usability and resource constraints.

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers.

Background Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. Methods We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. Results Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. Conclusions Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories: publication details, geographical distribution and uses, dataset features, threshold definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management.

Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world.

One of the major natural hazards the world is facing these days are floods. Malawi has not been spared. Floods have affected the countries’ socio-economic developmental plans. River gauges have been installed along major rivers to monitor water levels in a bid to warn communities of imminent flooding. In Malawi, ever since the installation of river gauges no study has been done to assess their effectiveness. This study examines the effectiveness of these river gauges as part of community-based early warning system. The research employs both qualitative and quantitative approach. Questionnaires, interviews, group discussions, document analysis were all used in order to understand the behavioural aspect of communities under study. The current community-based early warning system practices were benchmarked against the following elements: risk knowledge, technical monitoring and warning services, dissemination and communication of warnings and response capability. The study revealed that Malawi has two distinct systems in place: at national level (managed by several government departments) and at community level [managed by Civil Protection Committees (CPCs)]. These systems were installed by non-governmental organisations (NGOs) and faith-based organisations. Apparently, no direct link exists between the two. Operational bureaucracy affects the speedy presentation of warning messages at national level. Lack of capacity and necessities affects the operation of the community-based system. Despite the efforts to develop the early warning systems, the failures outweigh the successes. Government needs to provide enough funding for systems sustainability, build capacity of CPCs and install more technologically advanced systems.

Coastal flooding poses a significant threat to coastal communities, adversely affecting both safety and economic stability. This threat is exacerbated by factors such as sea level rise, rapid urbanization, and inadequate coastal infrastructure, as noted in recent climate change reports. Early warning systems (EWSs) have proven to be effective tools in coastal planning and management, offering a high cost-to-benefit ratio. Recent advancements have integrated operational numerical models with machine learning techniques to develop near-real-time EWSs, leveraging data obtained from reputable databases that provide reliable hourly sea-state and sea level data. Despite these advancements, a stepwise methodology for selecting representative events, akin to wave input reduction methods used in morphological modeling, remains undeveloped. Moreover, existing methodologies often overlook the significance of compound extreme events and their potential increased occurrence under climate change projections. This research addresses these gaps by introducing a novel input schematization method that combines efficient hydrodynamic modeling with clustering algorithms. The proposed methodοlogy, implemented in the coastal area of Pyrgos, Greece, aims to select an optimal number of representative sea-state and water level combinations to develop accurate EWSs for coastal flooding risk prediction. A key innovation of this methodology is the incorporation of weights in the clustering algorithm to ensure adequate representation of extreme compound events, also taking into account projections for future climate scenarios. This approach aims to enhance the accuracy and reliability of coastal flooding EWSs, ultimately improving the resilience of coastal communities against imminent flooding threats.

Fatalities from landslides are rising worldwide, especially in cities in mountainous regions, which often expand into the steep slopes surrounding them. For residents, often those living in poor neighborhoods and informal settlements, integrated landslide early warning systems (LEWS) can be a viable solution, if they are affordable and easily replicable. We developed a LEWS in Medellín, Colombia, which can be applied in such semi-urban situations. All the components of the LEWS, from hazard and risk assessment, to the monitoring system and the reaction capacity, were developed with and supported by all local stakeholders, including local authorities, agencies, NGO’s, and especially the local community, in order to build trust. It was well integrated into the social structure of the neighborhood, while still delivering precise and dense deformation and trigger measurements. A prototype was built and installed in a neighborhood in Medellín in 2022, comprising a dense network of line and point measurements and gateways. The first data from the measurement system are now available and allow us to define initial thresholds, while more data are being collected to allow for automatic early warning in the future. All the newly developed knowledge, from sensor hardware and software to installation manuals, has been compiled on a wiki-page, to facilitate replication by people in other parts of the world. According to our experience of the installation, we give recommendations for the implementation of LEWSs in similar areas, which can hopefully stimulate a lively exchange between researchers and other stakeholders who want to use, modify, and replicate our system.