A global database of historic and real-time flood events based on social media

Global systematical and comprehensive overview of mountainous flood risk under climate change and human activities

Snow-covered mountainous regions are crucial for the hydrologic cycle. Any changes in the cryosphere are critical and directly impact the hydrologic cycle and socio-environment of the downstream. It is likely to occur more extreme events of precipitations, raising the risk of flooding worldwide. Glacier melting is increasing, thus the formation of the moraine-dammed lake called glacial lake, whose outburst may be a catastrophic disaster. Due to steep topography, flash floods with high energy can sweep away infrastructure, electric power stations, property, and livelihood and even change the channel morphology, hence the whole environment. In this article, we present the causes of flooding in mountainous regions and historical trends of mountainous flooding and its management policies. Carbon emission is a driver to increase the temperature of the globe and which is triggering the flash floods in mountainous regions is illustrated using data from different sources. The discussion section includes how technology helps to achieve a climate-resilient environment. Understanding river morphology, mapping and monitoring risks, and simulating essential natural processes are necessary for reducing the cascading hazards in the mountains. There is still a gap in modern data collection techniques in mountainous regions. More advanced technology for regional and global collaborations, climate change adaption, and public awareness can build the climate resilience cryosphere.

Assessing post-abortion care using the WHO quality of care framework for maternal and newborn health: a cross-sectional study in two African hospitals in humanitarian settings

BACKGROUND

Abortion-related complications remain a main cause of maternal mortality. There is little evidence on the availability and quality of post-abortion care (PAC) in humanitarian settings. We assessed the quality of PAC in two hospitals supported by an international organization in Jigawa State (Nigeria) and Bangui (Central African Republic, CAR).

METHODS

We mapped indicators corresponding to the eleven domains of the WHO Maternal and Newborn Health quality-of-care framework to assess inputs, processes (provision and experience of care), and outcomes of PAC. We measured these indicators in four components of a cross-sectional multi-methods study: 1) an assessment of the hospitals' PAC signal functions, 2) a survey of the knowledge, attitudes, practices, and behavior of 140 Nigerian and 84 CAR clinicians providing PAC, 3) a prospective review of the medical records of 520 and 548 women presenting for abortion complications and, 4) a survey of 360 and 362 of these women who were hospitalized in the Nigerian and CAR hospitals, respectively.

RESULTS

Among the total 27 PAC signal functions assessed, 25 were available in the Nigerian hospital and 26 in the CAR hospital. In both hospitals, less than 2.5% were treated with dilatation and sharp curettage. Over 80% of women received blood transfusion or curative antibiotics when indicated. However, antibiotics were given to about 30% of patients with no documented indication. Among discharged women in CAR, 99% received contraceptive counseling but only 39% did in Nigeria. Over 80% of women in Nigeria reported positive experiences of respect and preservation of dignity. Conversely, in CAR, 37% reported that their privacy was always respected during examination and 62% reported short or very short waiting time before seeing a health provider. In terms of communication, only 15% felt able to ask questions during treatment in both hospitals. The risk of abortion-near-miss happening ≥ 24h after presentation was 0.2% in Nigeria and 1.1% in CAR. Only 65% of women in the Nigerian hospital and 34% in the CAR hospital reported that the staff provided them best care all the time.

CONCLUSION

Our comprehensive assessment identified that these two hospitals in humanitarian settings provided lifesaving PAC. However, hospitals need to strengthen the patient-centered approach engaging patients in their own care and ensuring privacy, short waiting times and quality provider-patient communication. Health professionals would benefit from instituting antibiotic stewardships to prevent antibiotic-resistance.

Citizen scientists' engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015)

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.

Global evidence of rapid urban growth in flood zones since 1985

Disaster losses are increasing and evidence is mounting that climate change is driving up the probability of extreme natural shocks1-3. Yet it has also proved politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local and national authorities4,5. However, locally determined patterns of urbanization and spatial development are key factors to the exposure and vulnerability of people to climatic shocks6. Using high-resolution annual data, this study shows that, since 1985, human settlements around the world-from villages to megacities-have expanded continuously and rapidly into present-day flood zones. In many regions, growth in the most hazardous flood zones is outpacing growth in non-exposed zones by a large margin, particularly in East Asia, where high-hazard settlements have expanded 60% faster than flood-safe settlements. These results provide systematic evidence of a divergence in the exposure of countries to flood hazards. Instead of adapting their exposure, many countries continue to actively amplify their exposure to increasingly frequent climatic shocks.

Flood exposure and poverty in 188 countries

Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global estimates of the number of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-year floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world’s flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.

Floods are most devastating for those who can least afford to be hit. Globally, 1.8 billion people face high flood risks; 89% of them live in developing countries; 170 million of them live in extreme poverty making them most vulnerable.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Satellite imaging reveals increased proportion of population exposed to floods

Flooding affects more people than any other environmental hazard and hinders sustainable development^1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods³. Where and how floods occur and who is exposed are changing as a result of rapid urbanization⁴, flood mitigation infrastructure⁵ and increasing settlements in floodplains⁶. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty^3,7-11. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255-290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58-86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates⁷. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods.

A Mixed-Methods Investigation into Barriers for Sharing Geospatial and Resilience Flood Data in the UK

With increases in average temperature and rainfall predicted, more households are expected to be at risk of flooding in the UK by 2050. Data and technologies are increasingly playing a critical role across public-, private- and third-sector organisations. However, barriers and constraints exist across organisations and industries that limit the sharing of data. We examine the international context for data sharing and variations between data-rich and data-sparse countries. We find that local politics and organisational structures influence data sharing. We focus on the case study of the UK, and on geospatial and flood resilience data in particular. We use a series of semi-structured interviews to evaluate data sharing limitations, with particular reference to geospatial and flood resilience data. We identify barriers and constraints when sharing data between organisations. We find technological, security, privacy, cultural and commercial barriers across different use cases and data points. Finally, we provide three long-term recommendations to improve the overall accessibility to flood data and enhance outcomes for organisations and communities.

A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma

Streaming social media provides a real-time glimpse of extreme weather impacts. However, the volume of streaming data makes mining information a challenge for emergency managers, policy makers, and disciplinary scientists. Here we explore the effectiveness of data learned approaches to mine and filter information from streaming social media data from Hurricane Irma's landfall in Florida, USA. We use 54,383 Twitter messages (out of 784K geolocated messages) from 16,598 users from Sept. 10 - 12, 2017 to develop 4 independent models to filter data for relevance: 1) a geospatial model based on forcing conditions at the place and time of each tweet, 2) an image classification model for tweets that include images, 3) a user model to predict the reliability of the tweeter, and 4) a text model to determine if the text is related to Hurricane Irma. All four models are independently tested, and can be combined to quickly filter and visualize tweets based on user-defined thresholds for each submodel. We envision that this type of filtering and visualization routine can be useful as a base model for data capture from noisy sources such as Twitter. The data can then be subsequently used by policy makers, environmental managers, emergency managers, and domain scientists interested in finding tweets with specific attributes to use during different stages of the disaster (e.g., preparedness, response, and recovery), or for detailed research.

Urban flooding events pose risks of virus spread during the novel coronavirus (COVID-19) pandemic

Since the first report in December 2019, the novel coronavirus (COVID-19) has spread to most parts of the world, with over 21.5 million people infected and nearly 768,000 deaths to date. Evidence suggests that transmission of the virus is primarily through respiratory droplets and contact routes, and airborne carriers such as atmospheric particulates and aerosols have also been proposed as important vectors for the environmental transmission of COVID-19. Sewage and human excreta have long been recognized as potential routes for transmitting human pathogens. The causative agent of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been detected in human feces and urine, where it could remain viable for days and show infectivity. Urban flooding, a common threat in summer caused by heavy rainfalls, is frequently reported in urban communities along with sewage overflows. With summer already underway and economy re-opening in many parts of the world, urban flooding and the often-accompanied sewage overflows could jeopardize previous mitigation efforts by posing renewed risks of virus spread in affected areas and communities. In this article, we present the up-to-date evidence and discussions on sewage-associated transmission of COVID-19, and highlighted the roles of sewage overflow and sewage-contaminated aerosols in two publicized events of community outbreaks. Further, we collected evidence in real-life environments to demonstrate the shortcuts of exposure to overflowed sewage and non-dispersed human excreta during a local urban flooding event. Given that communities serviced by combined sewer systems are particularly prone to such risks, local municipalities could prioritize wastewater infrastructure upgrades and consider combined sewer separations to minimize the risks of pathogen transmission via sewage overflows during epidemics.

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012–2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

Research of Impacts of the 2018 Hokkaido Eastern Iburi Earthquake on Sediment Transport in the Atsuma River Basin Using the SWAT Model

Landslides, debris flows, and other secondary disasters caused by earthquakes threaten the safety and stability of river basins. Earthquakes occur frequently in Japan. Therefore, it is necessary to study the impact of earthquakes on sediment transport in river basins. In this study, considering the influence of reservoirs, the Soil and Water Assessment Tool-calibration and uncertainty program (SWAT-CUP) was employed to analyze the runoff parameter sensitivity and to optimize the parameters. We manually corrected the sediment transport parameters after earthquake, using the Soil and Water Assessment Tool (SWAT) model to assess the process of runoff and sediment transport in the Atsuma River basin before and after the 2018 Hokkaido Eastern Iburi Earthquake. The applicability of the SWAT model to runoff simulation in the Atsuma River basin and the changes of sediment transport process after the earthquake were studied. The research results show that the SWAT model can accurately simulate the runoff process in the Atsuma River basin, the Nash–Sutcliffe efficiency coefficient (NSE) is 0.61 in the calibration period, and is 0.74 in the verification period. The sediment transport increased greatly after the earthquake and it is roughly estimated that the amount of sediment transport per unit rainfall increased from 3.5 tons/mm/year before the earthquake to 6.2 tons/mm/year after the earthquake.

Reaction of Juntendo Shizuoka Hospital at Izu Peninsula to Typhoon Hagibis (2019) and an Analysis of Twitter Concerning Izunokuni City

OBJECTIVE

This research was carried out to report the activity of the headquarters for disaster control at our hospital, and investigate the usefulness of obtaining information from Twitter when Typhoon Hagibis hit Izu Peninsula.

METHODS

First, we recounted the activity of the headquarters for disaster control. We then collected information from Twitter from October 12-14, 2019, using the keyword Izunokuni.' We took into consideration the contents of Twitter user posts as well as the number of reactions ('retweets' and 'likes'). Twitter information was classified into photo (+) and (-) groups, depending on whether or not the post had a photo or video included. The number of reactions between the two groups was then analyzed.

RESULTS

We counted 122 Twitter posts containing Hagibis-related information for Izunokuni City. The average number of both 'retweets' and 'likes' in the photo (+) group were significantly greater than those in the photo (-) group. All photos and videos depicted specific places in Izunokuni City and included actual footage of disaster scenes or local warning signs.

CONCLUSION

Based on our experience at the headquarters for disaster control during Typhoon Hagibis, Twitter was considered to be a useful tool for obtaining local disaster information based on its timeliness, reality and specificity.