The Precautionary Principles of the Potential Risks of Compound Events in Danish Municipalities

The risk of compound events describes potential weather and climate events in which the combination of multiple drivers and hazards consolidate, resulting in extreme socio-economic impacts. Compound events affecting exposed societies can therefore be deemed a crucial security risk. Designing appropriate preparation proves difficult, as compound events are rarely documented. This paper explores the understanding and practices of climate risk management related to compound events in specific Danish municipalities vulnerable to flood hazards (i.e., Odense, Hvidovre, and Vejle). These practices illuminate that different understandings of compound events steer risk attitudes and consequently decisions regarding the use of different policy instruments. Through expert interviews supported by policy documents, we found that the municipalities understand compound events as either a condition or situation and develop precautionary strategies to some extent. Depending on their respective geographical surroundings, they observe compound events either as no clear trend (Odense), a trend to be critically watched (Hvidovre), or already as a partial reality (Vejle). They perceive flood drivers and their combinations as major physical risks to which they adopt different tailor-made solutions. By choosing a bottom-up approach focusing on local governance structures, it demonstrated that the mismatch between responsibility and capacity and the ongoing separation of services related to climatic risks in the Danish municipality context need to be critically considered. The findings highlight that the complex challenge of compound events cannot be solved by one (scientific) discipline alone. Thus, the study advocates a broader inclusion of scientific practices and increased emphasis on local focus within compound event research to foster creative thinking, better preparation, and subsequently more effective management of their risks.

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.

Living With Floods: A Comparative Assessment of Resilience to Floods in Tal and Diara Regions of Malda District, West Bengal

Tal and Diara regions of Malda district are extremely prone to floods, still, report higher population density than the state's average density. This indicates that the local population has learned to live with floods by developing resilience to the flood threat through better preparedness, coping, and adaptive strategies. Such micro-level strategies developed by the local people can be useful to policymakers and social scientists alike in terms of better mitigating the flood menace and associated vulnerability. This paper attempts to measure and compare the level of flood resilience of the local people living in Tal and Diara at the household level. Furthermore, the article aims to analyse whether the distance from the major flood-causing rivers, viz. Fulhar in Tal and Ganga in Diara play a role in the degree of resilience of the population to floods. The paper is empirical, where information has been collected from sample households and focus group discussion with village elders in two sets of villages, i.e., Maniknagar and Ratua in Tal and Gopalpur and Nurpur in Diara. The UNDP technique has been used to compute the composite Resilience Index (RI) consisting of Preparedness Index (PI), Immediate Coping Index (ICI), and Adaptive Index (AI). The findings suggest that the villages located in and around the levees of major flood-causing rivers, viz. Maniknagar in Tal and Gopalpur in Diara records a higher level of resilience to floods in comparison to the interior villages, viz. Ratua in Tal and Nurpur in Diara. The paper concludes that in both Tal and Diara, people have learned to cope and adapt to floods and that the closer the distance from the major flood-causing rivers, the higher the villagers' resilience to flood hazards.

Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

Blockchain-Based Community Safety Security System with IoT Secure Devices

Humans frequently need to construct a huge number of buildings for occupants in large cities to work or live in a highly developed civilization; people who live in the same building or same area are defined as a community. A thief stealing items, a burglary, fire hazards, flood hazards, earthquakes, emergency aid, abnormal gas leakage, strange behavior, falling in a building, fainting in a building, and other incidents all threaten the community’s safety. Therefore, we proposed a blockchain-based community safety security system that is combined with IoT devices. In the proposed scheme, we designed multiple phases to process the alarm triggered by IoT devices. IoT devices can be set up in two types areas: private and public areas. Both types of IoT devices’ alarms have different process flow for the response and records checking phase. All records are saved in the Blockchain Center to assure the data can be verified and cannot be forged. During the communication between sender and receiver, we implemented some security methods to prevent message repudiation, prevent transmission intercept, prevent replay attacks, and ensure data integrity. We also implemented a clarifying mechanism to ensure that all system participants can have confidence in the system’s processing methods. The proposed scheme can be used in communities to improve community safety and prevent unnecessary conflicts.

Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data

Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.

Vulnerability and Adaptation to Flood Hazards in Rural Settlements of Limpopo Province, South Africa

Climate change has increased the frequency of extreme weather events such as heavy rainfall leading to floods in several regions. In Africa, rural communities are more vulnerable to flooding, particularly those that dwell in low altitude areas or near rivers and those regions affected by tropical storms. This study examined flood vulnerability in three rural villages in South Africa’s northern Limpopo Province and how communities are building resilience and coping with the hazard. These villages lie at the foot of the north-eastern escarpment, and are often exposed to frequent rainfall enhanced by orographic factors. Although extreme rainfall events are rare in the study area, we analyzed daily rainfall and showed how heavy rainfall of short duration can lead to flooding using case studies. Historical floods were also mapped using remote sensing via the topographical approach and two types of flooding were identified, i.e., those due to extreme rainfall and those due to poor drainage or blocked drainage channels. A field survey was also conducted using questionnaires administered to samples of affected households to identify flood vulnerability indicators and adaptation strategies. Key informant interviews were held with disaster management authorities to provide additional information on flood indicators. Subsequently, a flood vulnerability index was computed to measure the extent of flood vulnerability of the selected communities and it was found that all three villages have a ‘vulnerability to floods’ level, considered a medium level vulnerability. The study also details temporary and long-term adaptation strategies/actions employed by respondents and interventions by local authorities to mitigate the impacts of flooding. Adaptation strategies range from digging furrows to divert water and temporary relocations, to constructing a raised patio around the house. Key recommendations include the need for public awareness; implementation of a raft of improvements and a sustainable infrastructure maintenance regime; integration of modern mitigations with local indigenous knowledge; and development of programs to ensure resilience through incorporation of Integrated Development Planning.

Enhanced flood hazard assessment beyond decadal climate cycles based on centennial historical data (Duero basin, Spain)

Current climate modelling frameworks present significant uncertainties when it comes to quantifying flood quantiles in the context of climate change, calling for new information and strategies in hazard assessments. Here, state-of-the-art methods on hydraulic and statistical modelling are applied to historical and contemporaneous flood records to evaluate flood hazards beyond natural climate cycles. A comprehensive flood record of the Duero River in Zamora (Spain) was compiled from documentary sources, early water-level readings and continuous gauge records spanning the last 500 years. Documentary evidence of flood events includes minute books (municipal and ecclesiastic), narrative descriptions, epigraphic marks, newspapers and technical reports. We identified 69 flood events over the period 1250 to 1871, of which 15 were classified as catastrophic floods, 16 as extraordinary floods and 38 as ordinary floods. Subsequently, a two-dimensional hydraulic model was implemented to relate flood stages (flood marks and inundated areas) to discharges. The historical flood records show the largest floods over the last 500 years occurred in 1860 (3450 m3 s−1), 1597 (3200 m3 s−1) and 1739 (2700 m3 s−1). Moreover, at least 24 floods exceeded the perception threshold of 1900 m3 s−1 during the period (1500–1871). Annual maximum flood records were completed with gauged water-level readings (pre-instrumental dataset, PRE: 1872–1919) and systematic gauge records (systematic dataset, SYS: 1920–2018). The flood frequency analyses were based on (1) the expected moments algorithm (EMA) and (2) the maximum likelihood estimator (MLE) method, using five datasets with different temporal frameworks (historic dataset, HISTO: 1511–2018; PRE–SYS: 1872–2018; full systematic record, ALLSYS: 1920–2018; SYS1: 1920–1969; and SYS2: 1970–2018). The most consistent results were obtained using the HISTO dataset, even for high quantiles (0.001 % annual exceedance probability, AEP). PRE–SYS was robust for the 1 % AEP flood with increasing uncertainty in the 0.2 % AEP or 500-year flood, and ALLSYS results were uncertain in the 1 % and 0.2 % AEP floods. Since the 1970s, the frequency of extraordinary floods (>1900 m3 s−1) declined, although floods on the range of the historical perception threshold occurred in 2001 (2075 m3 s−1) and 2013 (1654 m3 s−1). Even if the future remains uncertain, this bottom-up approach addresses flood hazards under climate variability, providing real and certain flood discharges. Our results can provide a guide on low-regret adaptation decisions and improve public perception of extreme flooding.