Monitoring Of Natural Hazards Research Articles

Soil Moisture Forecast Using Transfer Learning: An Application in the High Tropical Andes

Soil moisture is a critical variable in the hydrological cycle and the climate system, significantly impacting water resources, ecosystem functioning, and the occurrence of extreme events. However, soil moisture data are often scarce, and soil water dynamics are not fully understood in mountainous regions such as the tropical Andes of Ecuador. This study aims to model and predict soil moisture dynamics using in situ-collected hydrometeorological data for training and data-driven machine-learning techniques. Our results highlight the fundamental role of vegetation in controlling soil moisture dynamics and significant differences in soil water balance related to vegetation types and topography. A baseline model was developed to predict soil moisture dynamics using neural network techniques. Subsequently, by employing transfer-learning techniques, this model was effectively applied to different soil horizons and profiles, demonstrating its generalization capacity and adaptability. The use of neural network schemes and knowledge transfer techniques allowed us to develop predictive models for soil moisture trained on in situ-collected hydrometeorological data. The transfer-learning technique, which leveraged the knowledge from a pre-trained model to a model with a similar domain, yielded results with errors on the order of 1×10−6<ϵ<1×10−3. For the training data, the forecast of the base network demonstrated excellent results, with the lowest magnitude error metric RMSE equal to 4.77×10−6, and NSE and KGE both equal to 0.97. These models show promising potential to accurately predict short-term soil moisture dynamics with potential applications for natural hazard monitoring in mountainous regions.

Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution

Abstract. In this publication we address the lack of technical expertise in the geoscience community in the design and construction of photogrammetric systems for monitoring natural hazards at high spatio-temporal resolution. Accordingly, we provide in-depth information on the components, assembly instructions, and programming codes required to build them, making them accessible to researchers from different disciplines who are interested in 3D change detection monitoring. Each system comprises five photographic modules and a wireless transmission system for real-time image transfer. As an alternative to lidar (light detection and ranging), high-end digital cameras offer a simpler and more cost-effective solution for the generation of 3D models, especially in fixed time-lapse monitoring systems. The acquired images, in combination with algorithms that allow the creation of improved 3D models, offer change detection performance comparable to lidar. We showcase the usefulness of our approach by presenting real-world applications in the field of geohazard monitoring. Our findings highlight the potential of our method to detect pre-failure deformation and identify rockfalls with a theoretical change detection threshold of only 3–4 cm, thereby demonstrating the potential to achieve similar accuracies to lidar but at a much lower cost. Furthermore, thanks to the higher data acquisition frequency, the results show how the overlap of events that leads to an erroneous interpretation of the behaviour of the active area is minimized, allowing, for example, more accurate correlations between weather conditions and rockfall activity.

Snow Attenuation of Infrasound Signals and Wind Noise

Abstract Infrasound monitoring of geophysical and human-sourced activity, such as explosions, eruptions, earthquakes, and avalanches, may be impacted by burial of sensors in a snowpack. We studied the frequency-dependent attenuation of sound, vertical sound speed, and wind noise reduction in a natural snowpack using earthquakes as a natural source of infrasound. Our experiment used a 2 m vertical distribution of sensors deployed during the 2021–2022 winter in the Sawtooth mountains of Idaho. We recorded 38 earthquakes and their primary infrasound propagating vertically upward through the snow. For these events, average attenuation appeared negligible between 0 and 8 Hz, rose to 0.1 dB/cm at ∼12 Hz, and remained at this level for higher frequencies. Using timing lags across the vertical profile the speed of infrasound in the snow was measured at 160 ± 51 m/s, significantly subsonic and indicative of pore-space waves. Finally, we quantified the significant reduction in the amount of background noise measured for snow-buried compared with free-air sensors. The 24 dB of reduction of noise observed for high wind speed conditions was evident with only 20 cm of snow burial and demonstrates the powerful capability of snowpack as a noise reduction filter. This study provides a clearer understanding of how snow burial filters infrasound and minimizes noise levels and has implications for monitoring of both natural and human-made hazards.

A Cost-Effective GNSS Solution for Continuous Monitoring of Landslides

The development of low-cost dual-frequency global navigation satellite system (GNSS) receivers in recent years has enabled the use of these devices in numerous applications. In the monitoring of natural hazards, such as landslides, these devices can be considered suitable sensors. In this work, dual-frequency GNSS receivers and antennas were used for setting up near-real-time continuous low-cost GNSS monitoring systems (LGMSs) under field conditions. The SimpleRTK2B board, which integrates the u-blox ZED-F9P dual-frequency GNSS chip and the survey-calibrated GNSS antenna are the main components of the GNSS system. The LGMS was installed and tested for six months in the Laze landslide located in the northwestern part of Slovenia. A total of four GNSS systems were deployed, three of which were located in pillars in the landslide itself and one in a stable area. Open-source software was used to postprocess the acquired data, providing daily coordinates in static relative and precise point positioning (PPP) positioning modes. The results of six months of near-real-time monitoring showed that the Laze landslide was stable during this period, with only minor changes in the vertical component. The trend of decreasing ellipsoid height was evident at all stations, although it was in the range of a few millimeters. To validate the results in static relative positioning mode, the coordinate differences between low-cost and high-end geodetic GNSS instruments were estimated and found to be in the range of 5 mm or less, while the difference between horizontal and spatial positions was less than 7 mm for all stations. The same data were processed in PPP, vertical displacements were not detected as in the static relative positioning mode due to the lower accuracy of the method itself. Considering the six-month performance of a low-cost GNSS system under field conditions, it can be emphasized that these devices are capable of performing near real-time continuous monitoring of slow movements with high accuracy and decreased costs. In addition, an experimental test was performed to identify the size of detected displacements in real-time kinematic (RTK). Based on the achieved results, it was concluded that 20 mm spatial displacements are detectable with LGMSs in RTK considering only 15 s of observations.

Urgent Computing for Protecting People From Natural Disasters

Urgent computing systems have become increasingly useful in supporting rapid alerting and forecasting frameworks associated with monitoring and management of natural hazards. High-performance computing systems offer effective computing environments for providing accurate prediction of disaster scenarios.

The Great Wall Vibration Monitoring of Traffic and Natural Hazards Using MEMS Accelerometers.

The stability of the Great Wall is mainly affected by traffic vibrations and natural hazards, such as strong winds, heavy rainfall, and thunderstorms, which are extremely harmful to the safety of the Great Wall. To determine the impact of the above factors on the Great Wall, a comparative analysis based on MEMS (micro-electro-mechanical system) accelerometer data was conducted between the non-impacts and the impacts of the above factors. An analysis of the relationship between vibration acceleration and each potential hazard based on a visual time series chart was presented using the data of accelerometers, traffic video, meteorology, rainfall, and wind. According to the results, traffic vibration is one of the primary dangerous factors affecting the stability of the Great Wall, Moreover, the intensity of the vibrations increases with the traffic flow. Thunderstorms also influence the stability of the Great Wall, with enhanced thunderstorm excitation resulting in increased vibration displacement. Furthermore, wind load is an influencing factor, with average wind speeds greater than 9 m/s significantly affecting the stability of the Great Wall. Rainfall has no impact on the stability of the Great Wall in the short term. This research can provide important guidance for risk assessment and protection of the Great Wall.

Revealing the shallow soil structure of the Yigong Lake in the Tibetan Plateau using a portable distributed acoustic sensing interrogator

The harsh and extreme environmental and near surface conditions of the Tibetan Plateau have limited the conventional electrical-based seismic instruments from obtaining high-quality seismic data through long-term and continuous observations, setting challenges for environmental seismology study and natural hazard monitoring in this area. Distributed acoustic sensing (DAS) is an emerging technique based on optical fiber communication and sensing. It provides a possible solution for subsurface imaging in extreme conditions at high spatiotemporal resolution by converting fiber-optic cables into dense seismic strainmeters. We deploy two survey lines with armored optical fiber cables in the Yigong Lake area, Southeastern Tibetan Plateau, to record ambient noise for a week. The DAS interrogator is specifically designed in a portable size with very low power consumption (25 W/h). Hence, we can use a 12V-DC battery for power supply to adjust the power limitation during the field recording. Ambient noise interferometry and multichannel analysis of surface waves are used to get 2D shear wave velocity profiles along the fiber paths. The results highlight the great potential of DAS for dynamic monitoring of the geological evolution of lakes and rivers in areas of extreme environments as in the Tibetan Plateau.

Atmospheric Anomalies Associated with the 2021 Mw 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites

The remote sensing-based Earth satellites has become a beneficial instrument for the monitoring of natural hazards. This study includes a multi-sensors analysis to estimate the spatial-temporal variations of atmospheric parameters as precursory signals to the Mw 7.2 Haiti Earthquake (EQ). We studied EQ anomalies in Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), and Outgoing Longwave Radiation (OLR). Moreover, we found EQ-associated atmospheric abnormalities in a time window of 3–10 days before the main shock by different methods (e.g., statistical, wavelet transformation, deep learning, and Machine Learning (ML)-based neural networks). We observed a sharp decrease in the RH and AP before the main shock, followed by an immense enhancement in AT. Similarly, we also observed enhancement in LST and OLR around the seismic preparation region within 3–10 days before the EQ, which validates the precursory behavior of all the atmospheric parameters. These multiple-parameter irregularities can contribute with the physical understanding of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) in the future in order to forecast EQs.

Evaluation of the Proton Contamination to MeV Electrons by Solar Proton Events Based on CSES Observations

AbstractThe high energy particle package (HEPP) onboard the China Seismo‐Electromagnetic Satellite (CSES) consists of three subsystems: HEPP‐H, HEPP‐L, and HEPP‐X. HEPP‐H and HEPP‐L detect 0.1–55 MeV electrons and 2–230 MeV protons. HEPP‐X is used to monitor 0.9–35 keV solar X‐rays. The primary objective of this study is to present the measurement techniques and instrument parameters, show the main features of particle observations recorded by these instruments and evaluate the proton contamination to MeV electrons. Based on CSES observation, we report a typical solar X‐ray and solar proton event and compare with observations from Polar Orbiting Environmental Satellites (POES) and Geostationary Operational Environmental Satellites (GOES) satellite. Based on the observations of solar proton events, we put forward one method to evaluate the proton contamination level using comparatively pure proton samples on high L‐shells where the proton flux is near zero at quiet time. The percentage of protons that are mistakenly identified to be electrons by HEPP‐H is estimated to be approximately 0.022%. The proton contaminations only contribute no more than 10% of the total observed electron populations at L = 4 with energy range 8–9 MeV in the outer radiation belt region. Therefore, we conclude that the on‐orbit performance of HEPP instrument has satisfyingly met the design expectations by providing plenty of high‐quality high energy particle observation data and reference for scientific uses in the fields of space weather research and natural hazard monitoring.

Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration

With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort at forecasting and mitigating hazards across the UK. In this paper, we present a case study of the Hollin Hill landslide in North Yorkshire where a variety of ground-based geophysical measurements are available for comparison with InSAR data. To include Sentinel-1 data in the UK’s Digital Environment, it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory (HHLO) is used by the British Geological Survey to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring. In July 2019, six corner reflectors were installed to improve the coherence of the InSAR measurements. We use Sentinel-1 InSAR data acquired between October 2015 and January 2019 to study the behaviour of this landslide, and find that the line-of-sight component of the down-slope movement is 2.7 mm/year in the descending track, and 7.5–7.7 mm/year in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide. Using InSAR data after the installation of the six corner reflectors, we are able to track the most recent movement on the landslide that occurred in January 2021. This result is in agreement with other ground-based measurements such as tracking of pegs, and soil moisture data derived from electrical resistivity tomography.

Site scale landslide deformation and strain analysis using MT-InSAR and GNSS approach – A case study

Surface deformation monitoring plays a vital role in understanding landslide kinematics and slope failure analysis. In the past, applications of precise geodetic techniques have been successfully demonstrated for surface deformation analysis in natural hazard monitoring. This study presents, first of its kind in India, site-scale deformation analysis of a half-century old active landslide (Jaggi Bagwan) in Rudraprayag district, Uttarakhand state (India). We present an approach to investigate the spatial variability of surface deformation (i.e. sectors with different kinematics within a landslide) using multi-temporal interferometric Synthetic Aperture radar (MT-InSAR) derived strain map. The maximum negative mean displacement rates obtained by MT-InSAR processing of 30 Ascending and 32 descending Sentinel-1A scenes are 27.56 mm/yr and 1.11 mm/yr, respectively. It indicates the presence of instability at the study site which is further supported by a line of sight (LOS) projected Global Navigation Satellite System (GNSS) displacement rates. Subsequently, a modified least square method is applied to generate strain map using 3D velocity components obtained by multi-geometry LOS decomposition. The interpretation of strain map is further assisted by the instabilities observed at the crown of Jaggi Bagwan using Google Earth imageries from 1996 to 2018 to better correlate the strain pattern. Strain rate map clearly identified the sectors with dominating extension and compression within landslide. The presented approach can be further extended to landslide risk reduction along national highways and valley blockages.

Facilitating Typhoon-Triggered Flood Disaster-Ready Information Delivery Using SDI Services Approach—A Case Study in Hainan

Natural disaster response and assessment are key elements of natural hazard monitoring and risk management. Currently, the existing systems are not able to meet the specific needs of many regional stakeholders worldwide; traditional approaches with field surveys are labor-intensive, time-consuming, and expensive, especially for severe disasters that affect a large geographic area. Recent studies have demonstrated that Earth observation (EO) data and technologies provide powerful support for the natural disaster emergency response. However, challenges still exist in support of the entire disaster lifecycle—preparedness, response, and recovery—which build the gaps between the disaster Spatial Data Infrastructure (SDI) already-in-place requirements and the EO capabilities. In order to tackle some of the above challenges, this paper demonstrates how to facilitate typhoon-triggered flood disaster-ready information delivery using an SDI services approach, and proposes a web-based remote sensing disaster decision support system to facilitate natural disaster response and impact assessment, which implements on-demand disaster resource acquisition, on-the-fly analysis, automatic thematic mapping, and decision report release. The system has been implemented with open specifications to facilitate interoperability. The typhoons and floods in Hainan Province, China, are used as typical scenarios to verify the system’s applicability and effectiveness. The system improves the automation level of the natural disaster emergency response service, and provides technical support for regional remote-sensing-based disaster mitigation in China.

Considerations and Multi-Criteria Decision Analysis for the Installation of Collocated Permanent GNSS and SAR Infrastructures for Continuous Space-Based Monitoring of Natural Hazards

Over the past few decades, the global population and the built environment’s vulnerability to natural hazards have risen dramatically. As a result, decisive actions, such as the SENDAI framework, have emerged to foster a global culture of successful disaster risk reduction policies, including actions to mitigate the social and economic impact of geohazards. The effective study of natural disasters requires meticulous and precise monitoring of their triggering factors, with ground- and space-based techniques. The integration of GNSS and SAR observations through the establishment of permanent infrastructures, i.e., Continuously Operating Reference Stations (CORS) networks and arrays of Corner Reflectors (CRs), may form a seamless ground displacement monitoring system. The current research literature provides fragmented guidelines, regarding the co-location of SAR and GNSS permanent infrastructures. Furthermore, there exist no guidelines for the determination of the most suitable locations using a holistic approach, in terms of criteria and required data. The purpose of this paper is to present a semi-automatic multicriteria site suitability analysis and evaluation of candidate sites for the installation of a permanent CORS and two CRs; one for each pass, taking into account various parameters and criteria. The first results demonstrate that the collocation of SAR and GNSS permanent infrastructures, utilizing a holistic criteria-based approach, is successful and complies with all the literature’s requirements.

UAV survey method to monitor and analyze geological hazards: the case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)

Abstract. Active geological processes often generate a ground surface response such as uplift, subsidence and faulting/fracturing. Nowadays remote sensing represents a key tool for the evaluation and monitoring of natural hazards. The use of unmanned aerial vehicles (UAVs) in relation to observations of natural hazards encompasses three main stages: pre- and post-event data acquisition, monitoring, and risk assessment. The mud volcano of Santa Barbara (Municipality of Caltanissetta, Italy) represents a dangerous site because on 11 August 2008 a paroxysmal event caused serious damage to infrastructures within a range of about 2 km. The main precursors to mud volcano paroxysmal events are uplift and the development of structural features with dimensions ranging from centimeters to decimeters. Here we present a methodology for monitoring deformation processes that may be precursory to paroxysmal events at the Santa Barbara mud volcano. This methodology is based on (i) the data collection, (ii) the structure from motion (SfM) processing chain and (iii) the M3C2-PM algorithm for the comparison between point clouds and uncertainty analysis with a statistical approach. The objective of this methodology is to detect precursory activity by monitoring deformation processes with centimeter-scale precision and a temporal frequency of 1–2 months.

A Bird’s‐Eye View on Ambient Infrasonic Soundscapes

AbstractA method is introduced to reconstruct microbarom soundscapes in absolute values. The soundscapes are compared to remote infrasound recordings from infrasound array I23FR (Kerguelen Island) and in situ recordings by the INFRA‐EAR, a biologger deployed near the Crozet Islands. The reconstruction method accounts for all‐acoustic contributions, divided into evanescent microbaroms (detectable directly above the source) and propagating microbaroms (detectable over long ranges). It is computed by integrating acoustic intensities over the ocean surface, convolved with the transfer function quantifying the propagation losses and propagation time. The reconstructed soundscapes are found within 2.7 dB for of the measurements in the microbarom band of 0.1–0.3 Hz. Infrasonic soundscapes are essential for understanding the ambient infrasonic noise field and are a basic need for applications, such as atmospheric remote sensing, natural hazard monitoring, and verification of the Comprehensive Nuclear‐Test‐Ban Treaty.

Development of a mobile application for Early Warning Systems and risk management in Cuba

Information and communication technologies (ICT) play a determining role in tackling climate change. ICTs are increasingly used in surveillance, prediction and support actions in adapting to these environmental risks. The archipelago of Cuba is one of the most vulnerable due to the negative effects of climate change. The island nation adopts the benefits offered by ICTs, in order to promote risk reduction due to the impact of natural and anthropogenic threats. The article presents the design of a computer tool called SATEMovil, which was conceived as a mobile phone application for receiving early alerts in the face of extreme threats. The research exposes the operation of the Early Warning Systems and mobile applications in Cuba and the process of design and creation of the SATEMovil App. The application contributes to adequate integrated risk management in Cuba, by favoring the identification and continuous monitoring of natural hazards, through textual reports and dynamic graphics. Its use optimizes the existing relationship, between social vulnerability indices and the level of uncertainty in the population, in the six identified threats. The result obtained enhances the country’s preparedness to reduce and confront risk.

The INFRA-EAR: a low-cost mobile multidisciplinary measurement platform for monitoring geophysical parameters

Abstract. Geophysical studies and real-time monitoring of natural hazards, such as volcanic eruptions or severe weather events, benefit from the joint analysis of multiple geophysical parameters. However, typical geophysical measurement platforms still provide logging solutions for a single parameter, due to different community standards and the higher cost per added sensor. In this work, the Infrasound and Environmental Atmospheric data Recorder (INFRA-EAR) is presented, which has been designed as a low-cost mobile multidisciplinary measurement platform for geophysical monitoring. In particular, the platform monitors infrasound but concurrently measures barometric pressure, accelerations, and wind flow and uses the Global Positioning System (GPS) to position the platform. Due to its digital design, the sensor platform can be readily integrated with existing geophysical data infrastructures and be embedded in geophysical data analysis. The small dimensions and low cost per unit allow for unconventional, experimental designs, for example, high-density spatial sampling or deployment on moving measurement platforms. Moreover, such deployments can complement existing high-fidelity geophysical sensor networks. The platform is designed using digital micro-electromechanical system (MEMS) sensors embedded on a printed circuit board (PCB). The MEMS sensors on the PCB are a GPS, a three-component accelerometer, a barometric pressure sensor, an anemometer, and a differential pressure sensor. A programmable microcontroller unit controls the sampling frequency of the sensors and data storage. A waterproof casing is used to protect the mobile platform against the weather. The casing is created with a stereolithography (SLA) Formlabs 3D printer using durable resin. Thanks to low power consumption (9 Wh over 25 d), the system can be powered by a battery or solar panel. Besides the description of the platform design, we discuss the calibration and performance of the individual sensors.

An Enhanced MOD16 Evapotranspiration Model for the Tibetan Plateau During the Unfrozen Season

AbstractEvapotranspiration (ET) is composed of soil evaporation (ETs), canopy transpiration (ETc), and canopy intercepted water evaporation (ETw), and it plays an indispensable role in the water and energy cycles of land surface processes. More accurate estimations of variations in ET are essential for natural hazard monitoring and water resource management. An improved algorithm for ETs with soil moisture and texture (SMT) and an optimized ETc based on the MOD16 model (MOD16‐STM) are proposed for the cold, arid, and semiarid regions of the Tibetan Plateau (TP). The nonlinear relationships between the soil surface resistance (rss) and soil surface hydration state in different soil textures were redefined by using five flux tower measurements over the TP. The value of the mean potential stomatal conductance per unit leaf area (CL) used for ETc calculation in grasslands was optimized at 0.0038 (m s−1). The MOD16‐STM was compared with the MOD16 and a soil water index‐based Priestley‐Taylor algorithm (SWI‐PT) at five independent sites. The results showed that the half‐hourly, daily, and monthly estimations from the MOD16‐STM were closer to observations than those from the other two models. The results indicated that the MOD16‐STM increased R2 by 0.2/0.05 and the index of agreement by 0.37/0.23 and deceased RMSE by 43.89/42.77 W m−2 and the absolute mean bias (|MB|) by 46.23/45.89 W m−2, when compared to the results of the MOD16 at daily/monthly scales. The results suggested that the MOD16‐STM will produce more accurate ET estimates over the flux sites and has great potential for ET estimation and land surface model improvement for the TP region.

Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.

Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

Extreme weather events are occurring more frequently as a result of climate change. In October 2019, eastern Japan was hit by Hagibis, a large and high-speed typhoon. This unprecedented typhoon caused the evacuation of over 4000 people, injured more than 300 people, and damaged more than 98,000 dwellings throughout the affected area. Because floods are one of the most devastating natural disasters in Asia, providing an effective early warning system (EWS) is critical to reducing disaster impacts. However, warnings based only on natural hazard monitoring do not offer sufficient protection. Integrating natural hazard monitoring and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. We analyzed time-series data including rainfall intensity, 90-min-effective rainfall, and river water level as well as Twitter data related to disaster events during the 5-day period from 11 to 15 October, focusing on the most affected areas in Japan. The analysis included more than 60,000 tweets. Our analysis confirmed the utility of the statistical approach of outbreak detection with social media data in the early detection and local identification of multiple-flood events, and the results from the municipality-level analyses show that tweet frequencies related to the flood disaster ontological categories were significantly correlated to temporal variations in the hazard monitoring data. Thus, flood detection at the administrative level using social media data combined with current hazard monitoring data can enable a decision-driven EWS design. Interactive approaches for decision-making and knowledge production should continue to be considered in the face of climate-change-induced disasters.