Rapid Impact Assessment Research Articles

Landslide Rapid Impact Assessment at Hydro Dam via THyCAS and UAS

The TNB Hydro Catchment Area Surveillance (THyCAS) is a web-based geographic information system (GIS) used to monitor hydropower dam catchment areas in Peninsular Malaysia. It can also be used for landslide rapid impact assessment by monitoring the surrounding landslide area. However, during the rainy season, satellite images are unable to provide the information required by users due to full cloud cover. Therefore, an Unmanned Aircraft System (UAS) is used to provide the data required by THyCAS by mapping the surrounding area affected by landslides and uploading the processed data in THyCAS. Thus, the user can get the required information regarding the landslide incident. Furthermore, with UAS Full Motion Video (FMV) data, the location of the collapsed power transmission towers can be acquired. In addition, any information on land changes that could jeopardise the surrounding dam area is gathered. Therefore, with the information from the rapid impact assessment provided by THyCAS and UAS data, a preliminary risk assessment plan can be established and implemented by the relevant parties. The results obtained from the rapid assessment can also be used as additional information to find out the actual cause of landslides in the area.

Rapid remote volcanic ashfall impact assessment for the 2022 eruption of Hunga volcano, Tonga: a bespoke approach and lessons identified

When disasters occur, rapid impact assessments are required to prioritise response actions, support in-country efforts and inform the mobilisation of aid. The 15 January 2022 eruption of Hunga volcano, Tonga, and the resulting atmospheric shockwave, ashfall, underwater mass disturbance and tsunami, caused substantial impacts across the Kingdom of Tonga. Volcanic impacts on the scale observed after the eruption are rare, necessitating a reliance on international advice and assistance. The situation was complicated by the loss of Tonga’s international submarine fibreoptic cable (causing a complete loss of communications for approximately 20 days) along with border closures due to the COVID-19 pandemic. A need emerged for a rapid remote volcanic impact assessment and provision of specialist advice to help inform the response of international partners. Here we present a novel methodology for conducting rapid remote volcanic ashfall impact assessments, conducted over a 10-day period following the eruption. We used three different hazard models for ashfall thickness across the main island of Tongatapu and available asset information and vulnerability functions for buildings, agriculture, electricity networks, water supply and roads, to provide initial estimates of losses due to ashfall from the 15 January eruption. For buildings, we estimated losses both as total losses and as percentages of the total replacement cost of buildings on Tongatapu. For agriculture, we made probabilistic estimates of production losses for three different crop classes. For ashfall clean-up, we estimated ranges of ashfall volumes requiring clean-up from road surfaces and roofs. For water supply, electricity networks and roads, our analysis was limited to assessing the exposure of important assets to ashfall, as we had insufficient information on system configurations to take the analysis further. Key constraints on our analysis were the limited nature of critical infrastructure asset inventories and the lack of volcanic vulnerability models for tropical regions including Pacific Island nations. Key steps towards iteratively improving rapid remote impact assessments will include developing vulnerability functions for tropical environments as well as ground-truthing estimated losses from remote approaches against in-person impact assessment campaigns.

Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.

Rapid impact assessment of severe cyclone storm Michaung along coastal zones of Andhra and Tamil Nadu, India: A geospatial analysis

The coastal regions of India, particularly the Bay of Bengal, are highly vulnerable to the severe weather conditions induced by tropical cyclones. This study presents a comprehensive analysis of the changes in vegetation cover, shoreline dynamics, and meteorological variations resulting from Cyclone Michaung and subsequent post-monsoon events along the coastal zones of Andhra Pradesh and Tamil Nadu, India. A suite of vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), and Disaster Vegetation Damage Index (DVDI), were employed to assess changes in vegetation cover. The Digital Shoreline Assessment System (DSAS) was utilized to evaluate shoreline changes, and a range of meteorological variables were analyzed to assess the impacts of Cyclone Michaung and post-monsoon events. The findings reveal significant ecological impacts, with a notable decrease in Very Healthy Vegetation from 5.71% to 1.30%. The mean value of mVCI shifted from −0.2 to −0.16, indicating vegetation stress. DVDI analysis showed that 56.49% of the area experienced moderate damage, while 40.24% suffered severe vegetation damage. Additionally, erosion was observed along 79.46% of the shoreline transects in the study area. These insights are critical for assisting coastal managers in developing resilient coastal systems. Remarkably, a significant change in rainfall was recorded between the pre-cyclone period and the landfall day, with maximum rainfall intensifying from 13.93 mm/h on December 3rd to 164.26 mm/h on December 4th, and subsequently decreasing to 144.39 mm/h on December 5th.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas.The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

A comparative analysis of landfill practice between Poland and Indonesia

This research addresses the global challenge of waste management (WM) by comparing landfill management practices in Poland (Radiowo landfill) and Indonesia (Kebon Kongok landfill) to define the differences in landfill WM systems and analyse the main challenges in implementing effective landfill management WM systems in Poland and Indonesia. The data was analysed by employing the rapid impact assessment matrix (RIAM). The analysis focused on three critical areas: gas management, leachate management and landfill reclamation. The results showed that the Polish landfill demonstrated high efficacy, particularly in leachate management with water quality impact (96), gas emissions control with methane extraction (96), and site reclamation for site restoration (96). For these purposes, the landfill uses advanced leachate treatment technologies and drainage systems. Conversely, Indonesian landfill practices show severe inefficiencies, with negative scores in almost all sectors. The lowest score was in leachate management with water quality impact (–48), gas emissions with air quality impact (–48), and site reclamation in community space development (–48). The study contributes to the sustainable waste management discourse, providing actionable insights for policymakers and environmental managers.

Environmental impact assessment with rapid impact assessment matrix method: during disaster conditions

In the last several decades, Iran’s ecosystem has suffered due to the careless usage of natural resources. Cities have grown in an uneven and non-normative way, and poor project management has been a major issue, particularly in large cities. An even greater number of environmental factors and engineering regulations are not relevant to projects. Because of this, in order to ascertain a project’s environmental impact, an environmental impact assessment (EIA), is required. Using the rapid impact assessment matrix (RIAM) is one method of applying it to EIA. Reducing subjectivity brings objectivity and transparency. During the COVID-19 pandemic, a thorough EIA was carried out for the Tehran project utilizing the RIAM and other possibilities. This research is the first to combine the methodology that was discussed during the incident. Through the use of the RIAM technique, the environmental impact of COVID-19 was to be quantified in this inquiry. The research examined lockdown procedures and the COVID-19 pandemic to create an EIA indicator. In a real-world case study conducted in Tehran, Iran, the impact of the initiative was evaluated using the RIAM methodology during the COVID-19 epidemic. The results demonstrated that COVID-19 had both beneficial and harmful effects. Decision-makers were effectively informed about the COVID-19 pandemic’s environmental consequences on people and the environment, as well as how to minimize negative effects, according to the EIA technique that used RIAM. This is the first research to integrate the EIA during a crisis, such as the COVID-19 pandemic, with the RIAM approach.

Enhanced earthquake impact analysis based on social media texts via large language model

Social media aids disaster response but suffers from noise, hindering accurate impact assessment and decision making for resilient cities, which few studies considered. To address the problem, this study proposes the first domain-specific LLM model and an integrated method for rapid earthquake impact assessment. First, a few categories are introduced to classify and filter microblogs considering their relationship to the physical and social impacts of earthquakes, and a dataset comprising 7282 earthquake-related microblogs from twenty earthquakes in different locations is developed as well. Then, with a systematic analysis of various influential factors, QuakeBERT, a domain-specific large language model (LLM), is developed and fine-tuned for accurate classification and filtering of microblogs. Meanwhile, an integrated method integrating public opinion trend analysis, sentiment analysis, and keyword-based physical impact quantification is introduced to assess both the physical and social impacts of earthquakes based on social media texts. Experiments show that data diversity and data volume dominate the performance of QuakeBERT and increase the macro average F1 score by 27 %, while the best classification model QuakeBERT outperforms the CNN- or RNN-based models by improving the macro average F1 score from 60.87 % to 84.33 %. Finally, the proposed approach is applied to assess two earthquakes with the same magnitude and focal depth. Results show that the proposed approach can effectively enhance the impact assessment process by accurate detection of noisy microblogs, which enables effective post-disaster emergency responses to create more resilient cities.

Assessing the Socio-Economic and Environmental Impact of Koidu Holdings on Local Communities in Tankoro Chiefdom, Kono District, Sierra Leone - Using the Rapid Impact Assessment Matrix Method

The study used the RIAM technique to evaluate environmental sustainability using an environmental impact assessment through nine sessions of focus group discussions in the affected mining communities. The system's methodology involves assigning scores to impact component issues based on predetermined criteria, which are then transferred into arrangements that indicate the extent of positive or negative effects. The RIAM analysis reveals that kimberlite diamond mining has predominant negative environmental impacts across all three communities, A, B, and C. However, certain social and economic components reflected positive changes. Nevertheless, the positive impacts on those three communities are just about the same. From the total scores summary for the three communities, it is obvious that the kimberlite mining in the affected communities have negative effects than the positive effects. Based on these findings, recommendations were made for improvement.

Sparse sensing to dense assessment: Incorporating spatial autocorrelation for assessing flood impacts.

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.

Environmental Impact Assessment of Onshore Wind Farms in the Region of Central Greece Using a Modified RIAM Method

Wind energy is one of the most basic forms of renewable energy, which shows an increasing rate of development worldwide and also at the European level. However, this rapid deployment of wind farms makes the need for an impact assessment of this type of projects on the natural and man-made environment imperative. The present paper aims to identify and assess the environmental impacts of wind farm projects in the Region of Central Greece. A modified Rapid Impact Assessment Matrix (RIAM) method is used for this purpose. The methodology includes the identification of the existing onshore wind farm projects in the study area, the appropriate modifications of the RIAM method to respond to the characteristics of the projects and the study area, the qualitative assessment of their potential impacts during construction and operational phases and the computation of the Environmental Performance Grade (EPG) of projects based on the pro-posed modified RIAM method. The results reveal that although there are some slight negative impacts on the natural environment of the study area, the examined wind farms contribute positively both to the atmosphere and to the socio-economic environment of the study. This study extends the potential for using RIAM as a tool in environmental impact assessment studies of renewable energy projects.

The Potential of Crowdsourced Data for the Rapid Impact Assessment of Large Earthquakes: The 2023 M 7.8 Kahramanmaraş-Pazarcık, Türkiye, Earthquake

Abstract Reliable and rapid impact assessment for large earthquakes is a challenge because it is difficult to rapidly determine the fault geometry and thus the spatial distribution of shaking intensities. In this retrospective study of the M 7.8 Kahramanmaraş-Pazarcık, Türkiye, earthquake, we evaluate how eyewitness observations crowdsourced through the LastQuake system can improve such assessments. These data consist of felt reports describing the local level of shaking or damage and manually validated geolocated imagery. In the first part of this study, the methods used to derive macroseismic intensity values from felt reports, particularly for high values, are validated by comparison with independently determined intensities. This comparison confirms that the maximum intensity that can be derived from felt reports does not generally exceed VIII. A fatality estimate of 3000 could be made within a few hours by evaluating the number of people exposed to high intensities using the felt reports and assuming a point source. However, this estimate was known to be an underestimate because of the point-source approximation; this underestimate was also confirmed by the geolocated imagery showing high levels of damage at epicentral distances well beyond those predicted by circular isoseismals. However, improved estimates could have been derived from the event’s ShakeMaps using the U.S. Geological Survey Prompt Assessment of Global Earthquakes for Response (PAGER) fatality loss-modeling system, either by incorporating the felt reports into the ShakeMaps computation or using, in addition, a finite-source (here line-source) model derived from the felt reports using the Finite-fault rupture Detector software. The inclusion of fault geometry would have resulted in a fatality estimate with data collected within 10 min of the origin determination, which was consistent with the final PAGER alert level and the reported death toll that were both only known days later. Although more work would be helpful to assess the reliability of the derived fault geometry, in regions where they are collected in large numbers, felt reports collected within 10 min of the earthquake can be used to substantially improve current fatality estimates.

Towards a dynamic earthquake risk framework for Switzerland

Abstract. Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction.

An evaluation of the hydropower planning alternatives at the upper reaches of Yellow River by considering carbon footprints

As a precautionary tool for decision-makings towards sustainable development, strategic environmental assessment (SEA) is quite promising in coping with climate change by taking the advantage of alternatives evaluation as early intervention and planning optimization. Despite of the acknowledged merits, the current alternatives evaluation has presented a weakness when tackling with the climate factors in practice due to an evident lack of carbon-related indicators. The challenge is even more pronounced in hydropower planning for the most sensitive response to climate, the most complicated impacts to evaluate and the recent doubts about the nature as a clean energy. Considering the assignable carbon emissions from the hydropower engineering including pre-treatment, construction, operation and decommission, six indicators of carbon footprint were selected and integrated with the conventional eco-environment and socio-economic considerations for evaluating four alternatives in the SEA of Upper Yellow River hydropower planning in China. The total 24 indicators were marked by the invited experts, and weighted by employing the BP neural network. The ranked marks from invited experts and improved by BP neural network weighting showed that the alternative II is the best among the four, indicating the slightest environmental impacts, the minimal carbon emissions, and the highest socio-economic benefits. This study delivered an improved framework based on the conventional rapid impact assessment method (RIAM) and carbon footprint assessment, and provided a practical technical solution for evaluating planning alternatives in the context of green hydropower development.

Planetary health: Creating rapid impact assessment tools

Addressing current environmental, social, health, and democratic challenges requires projects and evaluations to be conceptualized differently. This article proposes a pathway to creating rapid impact assessment tools that consider the dimensions that matter the most to ensure positive impacts for a thriving future. This work is based on three premises for evaluation practice, which needs to: contribute to a positive ecosystem, adopt a holistic perspective, and engage participants in deliberative and democratic practices. We first review related evaluation approaches—health impact and environmental impact assessments—to learn from these experiences and avoid their pitfalls. Second, we present and illustrate how the Planetary Health Rapid Impact Assessment tools can be developed and used. This article, is intended to inspire and support policymakers, program designers, decision-makers, administrators, and evaluators willing to positively influence planetary health and introduce such tools in their projects.

Multi-Dimensional Urban Flooding Impact Assessment Leveraging Social Media Data: A Case Study of the 2020 Guangzhou Rainstorm

In the contexts of global climate change and the urbanization process, urban flooding poses significant challenges worldwide, necessitating effective rapid assessments to understand its impacts on various aspects of urban systems. This can be achieved through the collection and analysis of big data sources such as social media data. However, existing literature remains limited in terms of conducting a comprehensive disaster impact assessment leveraging social media data. This study employs mixed-methods research, a synergy of statistical analysis, machine learning algorithms, and geographical analysis to examine the impacts of urban flooding using the case of the 2020 Guangzhou rainstorm event. The result show that: (1) analyzing social media content enables monitoring of the development of disaster situations, with varied distributions of impact categories observed across different phases of the urban flood event; (2) a lexicon-based approach allows for tracking specific sentiment categories, revealing differential contributions to negative sentiments from various impact topics; (3) location information derived from social media texts can unveil the geographic distribution of impacted areas, and significant correlations are indicated between the waterlogging hotspots and four predisposing factors, namely precipitation, proportion of built-up surfaces, population density, and road density. Consequently, this study suggests that collecting and analyzing social media data is a reliable and feasible way of conducting rapid impact assessment for disasters.

Assessing environmental health impacts of coal mining exploitation in Iran: A Rapid Impact Assessment Matrix (RIAM) approach for environmental protection

Environmental Impact Assessment is the process of evaluating the effects caused by a project on the environment. The outcomes generated by this assessment can lead to a reduction of the negative effects and an increase in the positive effects caused by mine projects. The present study was conducted to evaluate the environmental impact assessment of the Goliran Coal Mine in northern Iran. In the descriptive-analytical study, to achieve the objectives, observatory surveys were conducted around the coal mine using a checklist, which was about the positive and negative effects of a coal mine. Then the data were entered into the RIAM and the positive and negative effects were ranked and the most important effects were determined. In RIAM, one point is assigned to each component. 17 important activities for environmental impacts were identified using a checklist. Among the activities carried out at the coal mine site, the major ones included tunnel excavation, construction of the rail line collection and disposal of coal mine effluent, coal transportation, collection and disposal of mine tailings, and technical defects and leakage. The scores of each environmental factor were based on the four environmental components: physical/chemical, biological/ecological, social/cultural, and economic/operational. The results of the present study showed that the most negatively affected environmental components are the physical/chemical components derived from three activities; the construction of the underground tunnel; the construction of a coal transport rail line; and the actual transportation of coal extracts. The scores of each environmental factor based on the four components at the Goliran coal mine in northern Iran indicate that the highest negative score was -64, corresponding to the physical/chemical component, and was assigned to air pollution. On the other hand, the highest positive score corresponds to the economic/operational component with +54, assigned to the income that employees earn from the mine. Overall results showed that the coal mine in northern Iran had negative effects on the environment but the effects were not severe. It is suggested that for future research, corrective measures should be taken in the form of an environmental management plan to reduce the negative effects caused by coal mining, and then prospective research should be done to check the extent of reducing the negative effects.

Wood heater smoke and mortality in the Australian Capital Territory: a rapid health impact assessment.

To estimate the number of deaths and the cost of deaths attributable to wood heater smoke in the Australian Capital Territory. Rapid health impact assessment, based on fine particulate matter (PM2.5 ) data from three outdoor air pollution monitors and published exposure-response functions for natural cause mortality attributed to PM2.5 exposure. Australian Capital Territory (population, 2021: 454 000), 2016-2018, 2021, and 2022 (2019 and 2020 excluded because of the impact of extreme bushfires on air quality). Proportion of PM2.5 exposure attributable to wood heaters; numbers of deaths and associated cost of deaths (based on the value of statistical life: $5.3 million) attributable to wood heater smoke. Wood heater emissions contributed an estimated 1.16-1.73 μg/m3 to the annual mean PM2.5 concentration during the three colder years (2017, 2018, 2021), or 17-25% of annual mean exposure, and 0.72 μg/m3 (15%) or 0.89 μg/m3 (13%) during the two milder years (2016, 2022). Using the most conservative exposure-response function, the estimated annual number of deaths attributable to wood heater smoke was 17-26 during the colder three years and 11-15 deaths during the milder two years. Using the least conservative exposure-response function, an estimated 43-63 deaths per year (colder years) and 26-36 deaths per year (milder years) were attributable to wood heater smoke. The estimated annual equivalent cost of deaths was $57-136 million (most conservative exposure-response function) and $140-333 million (least conservative exposure-response function). The estimated annual number of deaths in the ACT attributable to wood heater PM2.5 pollution is similar to that attributed to the extreme smoke of the 2019-20 Black Summer bushfires. The number of wood heaters should be reduced by banning new installations and phasing out existing units in urban and suburban areas.

Rapid flood inundation mapping and impact assessment using Sentinel-1 SAR data over Ghaggar River basin of Punjab, India

Rapid flood inundation mapping and impact assessment using Sentinel-1 SAR data over Ghaggar River basin of Punjab, India

Integrated emergency management of ‘lifeline’ highway networks: lessons for interoperability

In 2021 the UK government published a review of lessons learned by the UK highways sector from extreme weather emergencies. As described in this paper, the review focused on the sector’s engagement with integrated emergency management and managing highways as critical ‘lifeline’ infrastructure. Focusing on robustness, reliability, redundancy, rapidity, resourcefulness and recovery, the review identified the need to develop consistent rapid impact assessment reporting, linked to regional- and national-level mutual aid contingencies. Underpinning this was the need to strengthen highway engineers’ ability to ‘prepare for their worst day, rather than their average day’.