Natural Hazards

Flooding is a major threat that presents a significant risk to human survival and development worldwide. Regarding flood risk management, flood modeling enables understanding, assessing, and forecasting flood conditions and their impact. This paper gives an overview of prevailing flood simulation models given their potentials and limitations for reflecting pluvial floods in watershed settings. The existing models are categorized into hydrologic, hydrodynamic, and coupled hydrologic-hydrodynamic models. The coupled hydrologic-hydrodynamic model can be further classified into full, external, and internal coupling models. The definitions, advantages, and limitations of each coupling model are discussed. It is found that the existing coupling types cannot accurately reflect the flood evolution processes. A dynamic bidirectional coupled hydrologic-hydrodynamic model is then detailed, where the watershed is spatially divided into inundation and non-inundation regions. These two regions are connected by a coupling moving interface. Only 2D hydrodynamic models are applied to the local inundation regions to ensure numerical accuracy, whereas the fully distributed hydrologic model is applied to non-inundation regions to improve computational efficiency. Future investigation should focus on the development of a dynamic bidirectional coupling procedure that can accurately represent the complex physical interactions between upstream rainfall-runoff and the local inundation process. This paper would help flood managers and potential users undertake effective flood modeling tasks, balancing their needs, model complexity, and requirements of input data and time.

Liquefaction is one of the most significant and remarkable causes of ground failure in geotechnical earthquake engineering. The phenomenon mostly occurs in saturated cohesionless soils when subjected to seismic loading. Studies on past liquefaction evidence, also known as paleoliquefaction studies, have helped several researchers predict a particular region’s future vulnerabilities. However, it is always difficult to prepare human life for future devastation resulting from ground failures. But, prior estimation of the magnitude and likelihood of earthquakes that may strike a location shortly can create an environment involving fewer risk factors. Several methods are available to back-calculate the strength of shaking and earthquake magnitude from seismic evidence, such as paleoliquefaction. Knowing the origin of an earthquake aids in locating the fault zone. As a result of these historical investigations, information for seismic hazard analyses and ground motion forecasts for a particular region becomes possible. The present study is designed on similar grounds to carry out the investigation. A total of nine sites are selected in the Roorkee region, which is vulnerable to earthquakes. The region is also prone to liquefaction based on experimental evidence available from the past studies. The Standard Penetration Test data analysis performed on all nine sites is used for site characterization. For probabilistic earthquake source characterization, magnitudes between 3.5 and 8.5 and PGA between 0.05 and 0.5 are considered. For the interpretation of the most likely source location and its corresponding likelihood of magnitude, both site and source data are utilized in the ground motion model. The findings show that with the increase in source-to-site distance, the likelihood of source occurrence reduces, whereas the most likely magnitude increases. Eventually, this framework illustrates a probabilistic method for determining the seismic source parameters based on paleoliquefaction inverse analyses.

Debris flows pose a serious threat to communities in mountainous areas, particularly in the years following wildfire. These events have been widely studied in regions where post-wildfire debris flows have been historically frequent, such as southern California. However, the threat of post-wildfire debris flows is increasing in many regions where detailed data on debris-flow physical properties, volume, and runout potential are sparse, such as the Southwest United States (Arizona and New Mexico). As the Southwest becomes more vulnerable to these hazards, there is an increasing need to better characterize the properties of post-wildfire debris flows in this region and to identify similarities and differences with nearby areas, particularly southern California, where there is a greater abundance of data. In this paper, we study the characteristics and downstream impacts of two post-wildfire debris flows that initiated following the 2021 Flag Fire in northern Arizona, United States. We gathered data regarding soil hydraulic properties, rainfall characteristics, watershed response, and debris-flow initiation, runout, volume, grain size, and downstream impacts during the first two monsoon seasons following the containment of the Flag Fire. We also applied established debris-flow runout and volume models that were developed in southern California to our study watershed and compared the output with observations. In the first monsoon season following the fire, there were two post-wildfire debris flows, one of which resulted in damage to downstream infrastructure, and one major flood event. We found that, while more intense rainfall is required to generate debris flows at our study site compared to southern California, burned watersheds in northern Arizona are still susceptible to debris flows during storms with low recurrence intervals in the first year following fire. During the second monsoon season, there were no major runoff events, despite more intense storms. This indicates that the temporal window for heightened debris-flow susceptibility at our study area was less than one year, due to the recovery of soil hydraulic properties and vegetation regrowth. We also found that the debris-flow properties at our study site, such as volume, mobility, and grain size distribution, may differ from those in other regions in the western United States, including southern California, potentially due to regional differences in rainfall characteristics and sediment supply. Differences in rainfall characteristics and sediment supply may have also influenced the performance of the debris-flow runout and volume models, which overpredicted the observed runout distance by 400 m and predicted a volume more than 17 times greater than what was observed.

Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides, vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate landslides susceptibility areas of Lushoto district, Tanzania. RS assisted in providing remote datasets including; Digital Elevation Models (DEMs), Landsat 8 OLI imageries, and past spatially distributed landslides coordinate with the use of a handheld Global Position System (GPS) receiver, while various GIS analysis techniques were used in the preparation and analysis of landslides influencing factors hence, generating landslides susceptibility areas index values. However, rainfall, slope angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have a direct influence on the occurrence of landslides in the study area. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which a 0.086 (8.6%) Consistency Ratio (CR) was attained (highly accepted). Findings reveal that rainfall (29.97%), slopes’ angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high influence on the occurrence of landslides, while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%), and NDVI (1.69%) had very low influences, respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis techniques indicate that about 97669.65 Hectares (ha) of land are under very low levels of landslides susceptibility, which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%), moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%), while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%), respectively. Furthermore, 81% overall model accuracy was obtained as computed from the Area Under the Curve (AUC) using Receiver Operating Characteristic (ROC) curve.

In the Three Gorges reservoir region, central China, seismic activity increased substantially after the reservoir impoundment in 2003 which continues till date. Previous studies show that these are reservoir-triggered earthquakes and various factors are responsible for the increase in seismic activity after the reservoir impoundment. However, these studies do not provide a comprehensive assessment of influence of reservoir water level variations on spatiotemporal distribution of earthquakes. In this study, we statistically analyze the influence of the water level variations on the increased seismic activity in the reservoir region for the period from May 2003 to April 2020, using the power spectrum and singular spectrum techniques. Our statistical analyses confirm the influence of long-term variations in the water level time series on the occurrence of earthquakes after the reservoir impoundment. The analysis also indicates a positive role of annual reservoir water level fluctuations in the total seismicity of the region. Depending on the cluster patterns and relationship with faults, the earthquakes of the Three Gorges reservoir region are divided into three seismic zones (A, B, and C). For zone C, both the power spectrum and singular spectrum analyses confirm the strong periodic influence of reservoir water level variations on the earthquakes. Increase in seismicity of zone B is only in the initial period but not in the later stages of water impoundment and our statistical analyses indicate that the seismicity of this zone is not directly related to the annual reservoir water level variations. This confirms the conjecture in the earlier studies that the seismicity of this zone is related to the collapse of coal mines present in the area in the initial stages of reservoir impoundment. For zone A, our statistical analyses do not show strong influence of the annual reservoir water level variations on the occurrence of earthquakes. We suggest that this is due to the contribution of various other factors along with reservoir impoundment in the occurrence of earthquakes in this zone, as also opined in some earlier studies.

China’s large- and medium-sized cities have entered the stage of seeking space resources underground. The safety of underground spaces has become an important issue for the high-quality development of cities. Urban ground collapse (UGC) is a common accident that occurs in underground spaces and is affected by a combination of factors. In this study, Internet news was used to supplement the data of the relevant departments, and a database of 68 UGC events from 1964 to the present was built. The probability distribution of the UGC risk was calculated using the nearest neighbor and density models. Then, the geo-detector was used to explore the explanatory power of risk factors, including natural environmental and socio-economic factors. The results show that the impact of the explanatory power of risk factors on UGC, from high to low, is population density, metro influence, and soil type. The interaction of factors enhances the explanatory power, whereby the interaction between socio-economic and natural environmental factors represented by population density and soil type is the highest. A risk division map of UGC in Hangzhou was obtained, with proportions of type I (0.6%), type II (28.3%), and type III (71.0%) areas. This study demonstrates the influence of human activities on UGC events through quantitative results and provides research support for urban underground safety risk assessment.

Flood susceptibility mapping is required for assessing flood risk areas and developing flood prevention techniques. The Thamirabarani river basin, a flood-prone area in the Tamil Nadu region of Srivaikundam, was investigated. Flood risk assessment using a composite risk and vulnerability index is a well-established tool that plays an important role in the development of flood risk reduction schemes. The present research is an attempt to analyze flood risk using analytical hierarchy procedures in a geographic information system context, which includes flood hazard components and susceptibility indicators. Geographic information system (GIS) are currently a trusted and useful tool for defining flood susceptibility maps at various spatial scales. The accuracy of various GIS-based flood risk assessment techniques is compared in this article. Land use and land cover, drainage density, topographic wetness index, distance from rivers, river length, slope, DEM, and rainfall were the eight foundation layers that were generated from the geographical database. All of the thematic layers and the resulting flood frequency map were combined to create the flood susceptibility using a GIS platform. Flood-vulnerable areas have been classified as very low class (1.7%), low class (26.3%), medium class (42.1%), high class (24%), and very high class (5.9%). The flood susceptibility study with this model will be a very beneficial and efficient tool for creating flood mitigation measures, according to local government administrators, researchers, and planners.

Water inrush at roof area seriously affects the safety of coal mines. The characteristics of aquifer and aquiclude at Wutongzhuang Mine are analyzed. Considering the effect of seepage field, a formula for calculating the height of water-conducting fractured zone (HWCFZ) in deep buried thick coal seam mining is derived. A damage-seepage coupling model with rock porosity and damage factor as independent variables is established. FLAC3D is re-developed by using FISH language, and the fluid–solid coupling calculation model of deep buried thick coal seam mining is established. The evolution law of the plastic zone, seepage field and water-conducting fractured zone (WCFZ)of the overburden in the gob with the advancement of the working face is analyzed, the main conclusions are as follows: With the continuous advancement of the working face, the distribution shape of the plastic zone and seepage field has changed from a trapezoidal to a saddle shape; when the working face reaches full mining, the maximum heights of the caving zone, fractured zone and HWCFZ are 24 m, 113 m, and 123 m, respectively; The 50 m-thick sandy shale aquifer is penetrated by the WCFZ, and the WCFZ on the side of the working face above the gob is the main water channel when the working face is advanced to 220 m. The on-site monitoring results showed that mine water inflow is not affected by surface rainfall and the 50 m-thick sandy shale is successively connected by the WCFZ. The results of comprehensive research showed that the HWCFZ cannot be calculated by traditional formulas when mining deep buried thick coal seams.

Some landslide susceptibility modeling uses idealized landslide points or buffer circles as landslide boundaries, adding uncertainty to the susceptibility modeling. However, landslide boundaries and their spatial shapes are typically presented as irregular polygonal surfaces, such as semicircles and bumps. To study the influence of different landslide boundaries on modeling uncertainty, 370 landslides and 11 environmental factors in Ruijin were chosen in order to establish landslide boundaries and their frequency ratio correlations with environmental factors. Then, these borders were formed, utilizing, respectively, landslide points, buffer circles, and precisely encoded and drawn polygons. Then, models like Point, Circle, and Polygon-based DBN and RF were built using deep belief network (DBN) and random forest (RF). Finally, the distribution pattern of the susceptibility index and its variability were used, along with the receiver operating characteristic (ROC) accuracy, to analyze the modeling uncertainty. The results indicate that: (1) while correct landslide polygon borders are more successful in ensuring modeling accuracy and dependability, using landslide points or buffer circles as boundaries can increase modeling uncertainty. (2) but the. (3) in the absence of precise landslide borders, the landslide susceptibility results derived by employing points and buffer circles as landslide barriers can reflect the spatial distribution pattern of landslide likelihood in the studied area as a whole.

Lava flows are one of the hazards involved in a volcanic eruption, and although they rarely cause the loss of human life, they are highly destructive in terms of damage to property and economic activity. Therefore, the management of volcanic disasters requires fast and accurate information on the behaviour and evolution of the flows, mainly related to their extension, displacement, and trajectory. This was the case during the disaster linked to the volcanic event that occurred on the island of La Palma in the Cumbre Vieja area at the end of 2021, which lasted eighty-five days. This paper describes part of the work performed by many different groups to provide predictive information aimed at feeding the early warning system set up during the disaster. This case shows the experience in the use of a proba-bilistic simulation algorithm implemented in the Q-LavHA plugin for the QGIS software, which is both easily accessible and applicable, to analyze its features in detail, as well as its predictive capacity. The results show that the model can efficiently and quickly satisfy the demand for this type of information, and its high similarity value is also validated by the Kappa index.

The exploding popularity of social networks provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to floods in 2020. The spatial distribution of floods was analyzed using Kernel Density Estimation. Public emotion was analyzed using Natural Language Processing tools. The association between floods and public emotion was explored through correlation analysis methods. The results indicated that: (1) Weibo texts could be utilized as effective data to identify urban waterlogging risk in Guangdong and Guangxi. (2) The waterlogging was mainly distributed in the southern part of Guangdong and Guangxi, especially in the provincial capitals and coastal cities. (3) Public emotion was predominantly negative, especially during periods of heavy precipitation. (4) There was a strong correlation between public emotion and floods in spatial–temporal variation. The degree of negative public emotion was significantly influenced by the number of waterlogging points. The presented results serve as the preliminary data for future planning and designing of emergency management. Graphical abstract

In the present study, deterministic and probabilistic approaches have been used for the assessment of liquefaction potential of ground during an earthquake. The deterministic approach was used to analyze and assess the liquefaction of loose saturated river bed deposit with emphasis on two benchmark locations. A wide range of earthquake data in the form of peak ground acceleration (PGA) values of 0.18 g, 0.37 g, 0.6 g and 0.75 g was used as input motions. The dynamic properties of soil were evaluated using standard penetration test (SPT) data obtained from the bore logs. The shear stress induced within soil deposit due to the seismic excitation was calculated in the form of cyclic stress ratio (CSR) and cyclic resistance ratio (CRR) in order to calculate the factor of safety (FOS) against liquefaction. In addition, liquefaction potential index (LPI) and probability of liquefaction (PL) were also calculated using input motion. It was observed, based on the probability analysis and liquefaction indices, that the shallow layer soil profile is safe against liquefaction, whereas deep layer soil profile is unsafe.

Hazard assessment is an important task in addressing disaster chain risk. According to the formation process of the three rainstorm-geohazard disaster chains, the disaster chain hazard values were transformed into the sum of the hazard values of the rainstorm and secondary geohazards. Multiple rainstorm scenarios were expressed using return periods and durations, and rainstorm intensity is obtained by fitting based on historical rainfall data. Combining DE-LightGBM and SMOTE-Tomek, the hazard values of secondary geohazards were calculated step by step by integrating rainstorm intensity, environmental factors and the formation process of secondary geohazards as input variables. Most of the study areas were classified as low hazard and very low hazard. The areas of medium hazard and high hazard have spatial agglomeration characteristics and were closely related to the distribution of the river system. The hazard distribution of disaster chains and secondary geohazards is basically positively correlated with rainstorm intensity. The hazard value characteristics of multi-level secondary geohazards can reflect the effect of amplifying damage by the disaster chain. The model shows good performance on the basis of RSME, accuracy and area under the curve.

To study the instability and failure mechanism of tunnel face in composite stratum and the evolution law of supporting pressure in the areas with spring, this paper used two different types of transparent soil and a self-designed 3D model test system. Six large transparent soil model tests were carried out by considering different confined water heads of spring and tunnel burial depth. The optical laser and high-speed camera were controlled to move on a high-precision linear platform. CT scanning was performed to obtain the failure model under different conditions. The finite element method considering a two-way fluid–structure coupling was used to validate the model test. The research results indicate under spring, the support pressure curves can be divided into three stages: rapid decline, rebound-rise, and constant. There is no rebound-rise stage under the condition of no spring. With increased confined water heads or reduced tunnel burial depth, limit support pressure shows an increasing trend. 2D and 3D damage models for different working conditions were obtained by PIV technology and 3D reconstruction technology. If there is a spring, the maximum displacement moves to the top of the tunnel with the increase of water head and the failure mode is a combination of “silo shape” and “inverted prism”. When there is no spring, the maximum displacement appears at the interface of the soil layer, and the failure mode is a combination of “silo shape” and “wedge shape”. The presence or absence of springs and the change of the confined water head have no significant effect on the height of the loosening area. With the tunnel burial depth ratio of 0.5 to 2.0, the height of the loose area increases from 0.17 to 0.83 D, and the soil arch area develops outward.

The effective adaptation of smallholders in regions severely affected by climate change is critical to their survival and development. This study provides insights into the climate change adaptation strategies of smallholders in the Yellow River-Huangshui River Valley (YHV), located on the eastern Tibetan Plateau (TP). The YHV is an important food-producing region on the TP. Climate change is threatening the livelihoods of local households. This study uses data from 494 household questionnaires, meteorological station data, and disaster statistics reports collected in the YHV region to understand the climate change adaptation strategies of smallholders and explore the factors that influence their strategies using the multivariate probit (MVP) model. The study found that frequent droughts, wind hail, and floods significantly impact agriculture in the YHV. Smallholders in the YHV adopted six main adaptation strategies to cope with the effects of climate change: crop rotation (88.25%), increasing agricultural inputs (75.30%), changing crop sowing times (61.94%), engaging in off-farm activities (50.20%), expanding cropland areas (32.59%), and raising more livestock (15.99%). The MVP model results indicated that smallholders' perceptions of disasters (drought, wind hail, and flood) have a significant impact on their adaptation strategies. An increase in perceived disasters positively and significantly affected off-farm strategies but negatively affected agricultural adaptation strategies. Although increasing the labor cost of agricultural production, the number of cropland plots to some extent encourages smallholders to adopt agricultural adaptation strategies and discourages the adoption of off-farm activities. Additionally, smallholder adaptation strategies were significantly influenced by various indicators including number of livestock, proportion of agricultural equipment, elevation, and off-farm income. The study proposes targeted policy recommendations to promote sustainable development of local households’ livelihoods. These include strengthening household coping capacities for droughts, wind hail and floods, promoting skills training, encouraging agricultural scaling operations and off-farm livelihood transformation for certain small farmers, and considering the environmental impacts of household adaptational strategies.

A probabilistic seismic hazard analysis of Pakistan is carried out to produce seismic hazard maps showing peak ground acceleration for the 2% and 10% of probability of exceedance in 50 years and spectral acceleration for the 5% and 2% probabilities of exceedance in 50 years. The main objective of this work is to define seismic hazard definition for Pakistan based on the classical approach using updated seismic hazards analysis inputs. A composite earthquake catalogue consisting of 32,700 events was compiled and used in the analysis with a magnitude range of Mw 4.0–8.3. The computations were made at a rectangular grid of 5 km in the OpenQuake engine. Ground motion parameter values have been obtained for flat rock reference seismic site conditions having a shear wave velocity of 760 m/s. Through the logic tree, epistemic uncertainties inherent in ground motion prediction equations and the maximum magnitude potential of seismic sources were estimated. Ground motion prediction equations were assigned equal weights in the logic tree while different weights were assigned to the maximum magnitude potential models. Results of the study were expressed as ground motion contour maps and median uniform hazard spectra for important cities in Pakistan. The results showed that peak ground acceleration values in Pakistan range from 0.16 to 0.54 g for the 10% of probability of exceedance, 0.23–0.72 g for the 5% probability of exceedance and 0.32–1.02 g for the 2% of probability of exceedance in 50 years. Spectral acceleration values at 0.2 s range from 0.67 to 2.19 g for the 2% chance of exceedance in 50 years, respectively, whereas spectral acceleration at 1.0 s ranges from 0.09 to 0.52 g for the 2% chance of exceedance in 50 years. The centre of Pakistan and the southern and the northern region are exposed to high hazard compared to the central-eastern part of the country. Quetta, Chitral, Peshawar, Islamabad Ziarat and Gwadar are exposed to seismic hazard in the PGA range of 0.32–0.45 g. Comparison of the results of this study with those of similar studies suggests that the seismic estimates proposed are reliable. The study provides a demonstration that the classical seismic hazard approach applied here is rational and applicable in Pakistan.

This work attempts to consider the prediction of slope failure patterns and failure depth in the slope failure early warning system. For this goal, 4,752 scenarios of slope instability with 11 kinds of soil properties under 432 designed intensity–duration (I-D) conditions are simulated to discuss the influences of rainfall conditions and soil shear strength parameters on the slope failure pattern and failure depth. Based on the simulation results of the slope failure depths and failure modes of 198 scenarios of slope failure, it is found that the four slope failure modes (Mode I, Mode II, Mode III, and Mode IV) are not distributed in a disorderly manner, but are concentrated in the four intervals of the I-D early warning threshold. Moreover, with the increase in soil effective cohesion and internal friction angle, the I-D early warning threshold moves up and to the right in the I-D two-dimensional plane. Furthermore, for the non-cohesive soil, the slope failure depths are concentrated in three regions with the generation of two jump points (at near 25 mm/h and 15 mm/h in this study) between the three regions. At these two jump points, a slight increase in rainfall intensity will result in a sharp decrease in slope failure depth, while for the cohesive soils, with the increase in soil effective cohesion, the two jump points gradually disappeared. Finally, based on the results of parametric analysis, two determination models of slope failure pattern are proposed, i.e., the “Boot Model” for cohesive soils and the “Eggplant Model” for non-cohesive soils. The findings in this study will promote the prediction of slope failure patterns and failure depth under different rainfall conditions and the assessment of the impact scope of collapsed soil.

This investigation is a study of the spatial propagation of a flood through the execution of two processes integrated into Geographic Information Systems to produce maps of potential flood zones. Based on Multicriteria Assessment (MCDA) techniques, the Analytical Hierarchy Process (AHP) is used to identify areas with different potential degrees of flooding, and the hydrodynamic behavior of the extension is analyzed in terms of flooding in relation to different maximum flows, using the Nays2D solver of the iRIC software. For MCDA, four essential parameters were used for the evaluation of flood zones in the middle and lower course of the Acha–Arica: land use, elevation, slope of the land and distance to the river were used for the establishment of potential zoning. While photogrammetry procedures and a high-resolution Digital Elevation Model, together with base calculation conditions such as roughness, edge, discharge and time, were used for the hydrodynamic simulation of flooding for four different maximum flows of 42, 57, 84 and 114 m³/s. The results indicate that the AHP technique allows quite a coherent prediction of flood zones to be made with a limited amount of data. Hydrodynamic simulation with Nays2D allowed a simulated graphic representation of the extension to be obtained that reflects conditions very close to the actual conditions in which these events take place. Validation was performed by comparing the results with each other and with other sources of documentary records which showed high similarities between flood zones obtained. Graphical abstract

On 21 June 2022 (at 20:54:34UTC), a magnitude 6.0 (Richter scale) earthquake (depth 4 km) struck eastern Afghanistan, devastating parts of the Khost and Paktika provinces. With its epicentre located southwest of Khost city, more than 1000 people were killed and several thousands injured by the earthquake. Deformation modelling and finite-fault source characterisation provide essential information for seismic hazard management and advanced analysis of the seismicity in earthquake prone areas. The coseismic deformation, which occurred west of the NNE–SSW-trending North Waziristan–Bannu thrust fault zone, was assessed using dual-pass (ascending and descending) interferometric Sentinel-1 data. The line of sight (LOS) displacement estimated from ascending pass imagery ranged from + 0.38 m to − 0.16 m. The descending pass LOS displacement ranged from + 0.10 m to − 0.13 m. The displacement components are resolved in vertical and east–west directions using ascending and descending passes. The displacement is predominantly westward with a strong upliftment component, thus indicating an SW-trending oblique slip movement of the fault. Inversion modelling was done to derive the seismic source characteristics from DInSAR displacement values using an elastic dislocation model. The linear inversion model converges at a single fault source solution with a dip and strike of ~ 62° and 216° N, respectively, having a rake of 25° N. The distributed slip values vary between 0 and 2.25 m. The inversion model results in a moment magnitude of 6.18 and a geodetic moment of 2.06 × 1018 Nm, comparable to those derived using teleseismic body wave data by USGS. Damage assessment using optical data from the Worldview-1 satellite substantiates that the building damages are located primarily within the zone of surface deformation.

Landslides are a major cause of earthquake damage, and the ability to anticipate seismically triggered landslide displacement is critical for seismic hazard assessment. The necessity for efficient measures for preventing and minimizing the damage caused by co-seismic landslides has prompted the development of innovative approaches for assessing areas exposed to seismically induced landslides at a regional scale. Uttarakhand is highly seismically active, and major geological formations of this region are heavily jointed or fractured. Landslides are common in this area, and the risk of earthquake-induced landslides is particularly significant due to the region's strong seismicity. The present study incorporated a combination of a probabilistic approach and modified Newmark’s method to obtain seismically induced landslide susceptibility maps. Firstly, a well-established probabilistic seismic hazard assessment method was utilized to calculate the probability of occurrence for various levels of earthquake shaking in terms of Arias intensity for different time intervals. Then by using an empirical equation based on Newmark’s displacement model, the slope strength demand was evaluated. The resulting slope strength demand values represent the minimal value of resistance required by a slope to maintain the probability of triggering an earthquake-induced landslide below a predetermined threshold. Finally, the spatial distribution of slope strength demand was compared with in-situ critical acceleration values computed using a modified Newmark method to determine slope failure probability. The obtained map presents a detailed demarcation of areas that will be affected by co-seismic landslide hazards in the future.

Reducing natural disasters and their related economic losses remains critical to achieving sustainable development. However, there is a lack of comprehensive studies that assess sustainable cities and human settlements in efforts to attain sustainable development goal 11.5. Here, the present research explains the effect of disaster risk and disaster resilience on human loss due to natural disasters (deaths, injured, and affected) in 90 countries spanning 1995 to 2019. We develop global risk and resilience indices through IMF index-making steps across 24 high, 24 upper-middle, 30 lower-middle, and 12 low-income countries. The negative binomial regression shows an increase in disaster-related loss to human beings (deaths, injured, and affected) due to disaster risk in all panels. The empirical results reveal a favorable impact of disaster resilience––resilience declines disaster-related losses in developed countries. We observe that focusing on basic infrastructure, economic stability, public awareness, hygiene practices, ICT, and effective institutions leads to disaster resilience, mitigation, and speedy post-disaster recovery. Due to the insignificant impact of resilience in developing countries, high-income countries could provide financial resources, modern and DRR technologies, especially to low-income economies. This study encourages countries to follow seven targets and four dimensions of the Sendai Framework to enhance disaster resilience.

In recent decades, many regions of the world have been affected by floods, which has caused a serious damage to rural communities, whose economy is based on agriculture and animal husbandry. One of the important dimensions in this field is the resilience of people in order to reduce the effects before, during and after the flood. Accordingly, the present study aimed to analyze rural people’s resilience in the face of floodwater (RPRF). For this purpose, a theoretical framework, whose main core is the VBN theory [including a chain of environmental attitudes (EAF), beliefs (BFF) and social norms (NFF) in facing floods], and also the variables of place attachment (PA), time perspective (TP), knowledge in the face of floods (KF), and organizational adaptation capacity (OAC) were used to analyze RPRF. This research is a descriptive-correlational and causal-relational type that was conducted with a survey. The statistical population included the villagers of Lorestan Province, Iran, who were affected by floods (N=6906), of which 330 people are selected as a sample. The research instrument was a questionnaire. The results of causal analysis indicated that TP (β=0.429), PA (β=0.333) and OAC (β=0.305) have the highest direct effect on RPRF, respectively. Also, EAF had the greatest non-causal effect (0.145) on RPRF. This study can be insightful for decision-makers and policy-makers to deal with floods in rural areas by emphasizing the variables of human ecology from the point of view of environmental psychology in the conceptualization of some basic elements effective on RPRF.

Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city’s ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people’s psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience.

Predicting the magnitude of induced earthquakes by underground injection is a critical strategy for risk assessment. This paper proposes the application of three machine learning techniques—support vector machine, probabilistic neural network, and AdaBoost algorithm—to predict the magnitude of the largest injection-induced earthquake (M) within a predetermined period. These machine learning techniques are used to model the relationships between ten input parameters—six seismicity indicators and four inputs related to injection wells—and earthquake magnitude classes (M < 3, 3 ≤ M < 4, and M ≥ 4). Models are applied to the earthquake and injection data for the Central Oklahoma region in the USA, and their input data are balanced using the data-level approach. The performance of each model is measured using the average recall of earthquake magnitude classes. The results show that balancing the training data improves the performance of the models, and the magnitude of induced earthquakes depends on the injection volume in the nine months before the earthquake prediction period. The parametric analysis of each model’s input reveals that induced earthquake magnitudes are more likely to occur when there are shorter distances between the bottom of injection wells and the crystalline basement. Among the investigated models, the support vector machine model trained on the data balanced using synthetic minority oversampling technique performed best by predicting an average of 72% of earthquake magnitude classes. Overall, the findings of this study will allow for predicting the magnitude of induced earthquakes and the development of an early warning system for policymakers and residents living in areas prone to injection-induced earthquakes.

Time-series InSAR technology has been widely applied to the identification of various types of surface deformation information, but its traditional processing chain of being applied to the detection, extraction and monitoring of wide-area geohazards requires a large amount of disk space, computing resources and processing time and has high requirements for data, personnel and equipment, which makes it difficult to promote its application in wide-area geohazard identification. Based on this background, this study constructs a time-series InSAR processing chain that makes it easy to achieve fast detection and effective extraction and monitoring of wide-area geohazards. First, the well-coherent HyP3 InSAR online service interferometric dataset is selected for time-series InSAR processing to detect the wide-area surface deformation rate. Then, the deformation anomaly regions in the wide-area range are extracted by combining multi-threshold segmentation and aggregation point (polygon) analysis methods. And then, the original Sentinel-1A SLC data are subjected to interferometric processing and time-series InSAR analysis using the open-source desktop program EZ-InSAR in the extracted typical small-area deformation anomaly regions of interest to obtain more refined deformation information. Nanjian County, Yunnan Province, China, is used as the study area, and a total of 119 potential geohazards with deformation anomalies were detected and extracted in this region, approximately half of which coincided with the results of the field survey and visual interpretation of optical images. The processing chain considers the advantages of wide-area detection of InSAR online service datasets and the advantages of raw resolution of SAR SLC data, and the data and software used are open source with low hardware requirements, which is expected to provide an effective processing chain for wide-area creep geohazard identification.

Natural disasters pose a negative impact not only on human lives but also on infrastructures such as healthcare systems, supply chains, logistics, manufacturing, and service industries. The frequency of such calamities has grown over time, which not only poses a threat to human survival and the living environment but is also detrimental to the economic growth and sustainable development of society. Earthquakes cause the most destruction compared to other natural disasters, especially in developing countries where the conventional reactive approach to dealing with disasters gives less chance for the appropriate utilization of already limited resources. Additionally, mismanagement of the resources and the lack of a unified action plan hinder the purpose of helping the grieving population. Considering the foregoing, this study presents a methodology for identifying hotspots and helping prioritize pre- and post-disaster management action by conducting a thorough seismic risk assessment while taking into consideration the case of a developing country as its focus. This methodology allows for rapid risk assessment against any given scenario by providing quantitative estimates of the repercussions such as physical damage to the buildings, casualties including injuries, economic losses, displaced households, debris, shelter requirements, and hospital functionality. In short, it could help prioritize actions with greater impacts and serve as a foundation for the formulation of policies and plans intended to increase the resilience of a resource-constrained community. Thus, the findings can be utilized by government agencies, emergency management organizations, non-government organizations, and aiding countries as a decision support tool.

The study on the strength characteristics of slip zone soils is an important part of landslide stability analysis. Current research on slip zone soils has concentrated on fine-grained slip zone soils. Meanwhile, investigations on slip zone soils that contain coarse particles are scarce. This study focuses on a review and analysis of the influence of specific factors, such as coarse-grained content, moisture content, and mineral composition, as well as the micro and meso aspects and numerical simulations on the strength characteristics of slip zone soils, based on a brief summary of the distribution range and geological characteristics of this type of slip zone soil. The concept of coarse-grained slip zone soils is proposed and compared with soil–rock mixtures. Research showed that the content, shape, size of the coarse particles, moisture content, and the composition and proportion of the clay minerals have an influence on the strength parameters of the slip zone soil. The deformation mechanism of the slip zone soil is further revealed from the perspective of particle deformation and movement at the micro- and meso-level, and the research method of combining macro, meso, and micro is emphasized. Finally, several aspects that should be strengthened in the research work are pointed out, such as the influence of coarse particle difference (content, shape, etc.) on the mechanical properties of the slip zone soil, the development process from meso-deformation to macro-deformation, the internal relationship between macro-parameters and meso-parameters, and the mechanical properties of weak layers of landslides in the permafrost regions.

Rapidly increasing population of Kolkata metropolitan city has led to encroachment in the marshy areas, mainly Salt Lake and Rajarhat in the outskirts of the city. Haphazard and unplanned construction in the city and on marshy land-filled regions has rendered the city vulnerable to liquefaction in the event of earthquakes. Liquefaction potential in terms of factor of safety (FS) for silty clay and silty sand has been evaluated by deterministic procedures of Boulanger and Idriss (Evaluating the potential for liquefaction or cyclic failure of silts and clays. Centre for Geotechnical Modelling. Report No. UCD/CGM-04/01, 2004; CPT and SPT based liquefaction triggering procedures. Centre for Geotechnical Modelling. Report No. UCD/CGM-14/01, 2014), respectively, for the study area. Probability of liquefaction (PL) values were determined using first order second moment (FOSM) reliability method and using ArcGIS 10.4.1 software, liquefaction hazard maps were constructed based on factor of safety and probability of liquefaction values at depths 7 m, 15 m and 25 m. Using nonlinear regression analysis, an attempt has been made to correlate PL with its corresponding FS values. Logistic function was utilized and PL–FS relations along with their fitted curves were obtained for silty clay, silty sand as well as for all soils considered in the present study. Sensitivity analysis was conducted on random variables, undrained shear strength (Su), N-value for equivalent clean sand ((N1)60CS), total overburden pressure (σv), effective overburden pressure (σ'v), maximum horizontal acceleration (amax), stress reduction factor (rd) and magnitude scaling factor (MSF), by increasing their coefficient of variation by 10%, 20% and 30%. Sensitivity index for all the parameters were determined which showed that Su and ((N1)60CS are the most sensitive while σv and σ'v being the least sensitive parameters.

Step-path failure is a typical unstable mode of rock slopes with intermittent joints, and the accurate prediction of their stability is of great significance. In the present study, an energy calculation programme for the slope system based on secondary development of two-dimensional particle flow code (PFC2D) was proposed. The step-path failure modes of slopes with intermittent joints were well reproduced and could be classified into three types according to the penetration modes of rock bridges: shear penetration, tensile penetration, and tensile–shear mixed penetration. The evolution of gravitational potential energy, elastic strain energy, and kinetic energy were also captured. Based on this, the failure criterion depending on the energy mutation was established. When the slope approached the critical instability state, the gravitational potential energy reduction, kinetic energy increment, and dissipative energy increment all increased suddenly, while the elastic strain energy increment suddenly decreased, indicating that the energy mutation could be used as the failure criterion for rock slopes with intermittent joints. The proposed energy mutation criterion has the advantages of clear physical meaning, strong integrity, easy judgement, and good applicability, which provides certain theoretical support for evaluation of rock mass stability and prediction of instability of jointed slopes.

Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.

Analysis of high-resolution seismic profiles from the central Beagle Channel enabled the recognition and characterization of several post-glacial subaqueous mass transport deposits in the subsurface of Bahía Ushuaia, offshore the city of Ushuaia (Argentina). These deposits are located at different stratigraphic levels and are embedded in the stratified sedimen-tary sequence within a deep trough, suggesting a recurrent occurrence. Up to eleven deposits have been identified, with four major events that involved estimated sediment volumes ranging from 12 to 57 million m 3. The latter are associated with megaturbidite deposits up to 10 m thick. Two of the largest events postdate the early Holocene unconformity of marine transgression. The seismic data suggest a different dynamic behaviour of these four main events, with erosional, strongly disintegrating and longer-lasting pre-marine transgression mass transport events compared to the post-early Holocene deposits. Several of the deposits appear to have a common origin due to earthquake-induced failures of submerged glaciofluvial deposits or from tributary deltas. In addition, a preliminary analysis of the potential generation of tsunami waves associated with the largest submarine failures was carried out using numerical models. The results show that at least three of them would have the potential to generate dangerous waves with maximum heights of up to 1-2 m in the port of Ushuaia and estimated run-up heights between 2 and 8 m. Although further detailed analysis is recommended, particularly with regard to a more sophisticated modelling approach and refinement of the resolution of bathymetric and topographic data for strategic areas.

The aim of this research was to investigate the validity and reliability of a Turkish version of the 26-item Australian Psychological Preparedness for Disaster Threat Scale (PPDTS). A cross-sectional study involving 530 university students and staff at Giresun University was conducted to establish the psychometric properties of the PPDTS. Content analysis, exploratory factor analysis, confirmatory factor analysis and Cronbach alpha values for reliability were used to analyse the data. Content analysis showed that one item needed to be dropped as it was not related to environmental threats to Turkish communities. The exploratory factor analysis indicated that 66% of the total variance was explained by three factors: (i) knowledge and management of the external situational environment, (ii) management of one’s emotional and psychological response, and (iii) management of one’s social environment. The confirmatory factor analysis showed acceptable overall goodness of fit for the three-factor model: CFI (0.908), RMSEA (0.074) for the 21 item scale. Cronbach's α coefficients of the subscales were 0.91, 0.93, and 0.83, respectively, while for the whole scale, it was 0.95. Four items from the original PPDTS were deleted in the course of the analyses. It was concluded that the Turkish version (PPDTS-T21) is a valid and reliable assessment tool for the evaluation of levels of psychological readiness for disaster threats to Turkish communities and will be useful in policy making for community preparedness for disaster events.

In the current century, wildfires have shown an increasing trend, causing a huge amount of direct and indirect losses in society. Different methods and efforts have been employed to reduce the frequency and intensity of the damages, one of which is implementing prescribed fires. Previous works have established that prescribed fires are effective at reducing the damage caused by wildfires. However, the actual impact of prescribed fire programs is dependent on factors such as where and when prescribed fires are conducted. In this paper, we propose a novel data-driven model studying the impact of prescribed fire as a mitigation technique for wildfires to minimize the total costs and losses. This is applied to states in the USA to perform a comparative analysis of the impact of prescribed fires from 2003 to 2017 and to identify the optimal scale of the impactful prescribed fire programs using least-cost optimization. The fifty US states are classified into categories based on impact and risk levels. Measures that could be taken to improve different prescribed fire programs are discussed. Our results show that California and Oregon are the only severe-risk US states to conduct prescribed fire programs that are impactful at reducing wildfire risks, while other southeastern states such as Florida maintain fire-healthy ecosystems with very extensive prescribed fire programs. Our study suggests that states that have impactful prescribed fire programs (like California) should increase their scale of operation, while states that burn prescribed fires with no impact (like Nevada) should change the way prescribed burning is planned and conducted.

In this reply to the discussion of our paper (Kumar et al. in Nat Hazards 116:2437–2455, 2023), we attempt to rebut the claims made regarding the use of the maximum sustained wind speed of a cyclone event irrespective of its location, inappropriate implementation of fitting methods, and inconsistent use of future projected sea surface temperature to historical records. While the discussion highlights certain observations about our results, the application of such techniques for evaluating PMTC parameters (adhering to AERB criteria) requires further research.

In this discussion, the authors will demonstrate that the large discrepancy of results in Kumar et al. (Nat Hazards 1–19, 2022) using different fitting methods is a cause of inappropriate implementation of some methods. The authors will also show the significant overestimate of the higher return period wind speeds using the basin-wide maximum sustained wind speeds compared to the results using simulations.

On September 18, 2022, an earthquake with a local magnitude (ML) 6.8 struck the southern part of Longitudinal Valley in southeastern Taiwan, resulting in the collapse and damage of many engineering structures. A field reconnaissance was conducted at the selected sites that experienced building and bridge damages and is presented in this paper. The focus is on geotechnical problems such as strong ground motion, ground rupture, soil liquefaction, and their influence on engineering structures. Strong motions of up to 0.6 g were induced, with similar intensity in the vertical and horizontal components near the epicenter. Widespread ground rupture traces were observed along the officially recognized active faults, inducing offsets up to tens of centimeters. Soil liquefaction was also noticed in this region, mainly on the river flood plain and characterized as gravel layer. The possible influence of these observed geotechnical characteristics on the damage pattern or failure mode of buildings, bridges, embankments, and levees was discussed and interpreted insightfully. The perspectives presented in this paper may serve as a reference to disaster prevention and mitigation in future events.

On-site investigation and numerical simulation are the main methods for assessing the risk of rockfall disasters. Unmanned aerial vehicles (UAVs) have been widely used because they can quickly obtain geospatial data. However, in a complex terrain environment, it is difficult to ensure reliable positioning accuracy and resolution of geospatial data collected or constructed at the same flight altitude. Therefore, it is difficult to complete the detailed investigation of rock mass characteristics, and the reliability of the simulation results of rockfall motion characteristics will also be affected. This article summarizes a new image acquisition method and three-dimensional (3D) modeling ideas. Furthermore, in this study a risk assessment is conducted of a slope where a rockfall disaster has occurred. Through this method, the real-world 3D model with a positioning accuracy of less than 5 cm and digital surface model (DSM) data with sub-centimeter resolution of the research area are obtained. Using these data, the rock mass characteristics and the mechanism of disaster formation are analyzed, and the motion characteristics of potential rockfall are numerically simulated in a geographic information system (GIS). After comparing and analyzing the simulated superior trajectory with the on-site terrain, it is found that the trajectory simulated using high-resolution terrain data can better represent the actual movement of falling rocks. The method and process summarized in this paper can provide technical reference for the investigation and evaluation of the risk of rockfall disasters under complex terrain conditions.

Jilin Province is one of the important grain-producing regions in China, the frequent drought and waterlogging events in this region have seriously impacted local agricultural production, therefore, it is particularly necessary to explore the spatiotemporal variations in drought and waterlogging and how they effect on maize yields. In this paper, we use the daily meteorological data recorded at 27 meteorological stations in Jilin Province from 1961 to 2020 to calculate the modified crop water deficit index (mCWDI), which were based on the daily crop coefficient (Kc) corresponding to different growth stages for each station. The spatiotemporal evolution processes of drought and waterlogging during the growing season in Jilin Province were analysed by the linear regression model, and the impacts of drought and waterlogging conditions on maize yields in Jilin Province under different growing seasons were quantified by means of the correlation analysis and multiple regression analysis methods. The results showed that the effective precipitation during the whole reproductive period showed a spatial distribution pattern of decreasing from southeast to northwest, with precipitation totals ranging from 335.02 to 677.38 mm, while the spatial distribution of the water demand showed the opposite trend. The south-eastern region of Jilin Province was in a state of water surplus, while the precipitation in other areas could not meet the water requirements of maize, resulting in decreasing drought frequency trends from northwest to southeast and from the early stage to the developmental stage of maize; in addition, increasing trends were observed in the middle and late reproductive stages of maize. The waterlogging frequency in the south-eastern region showed the spatial distribution characteristics of being higher in the early and late reproductive periods and lower in the development period of maize, and the growth rate of the maize-waterlogging frequency was higher in central Jilin Province than in other areas. Moreover, drought showed a more significant negative correlation with the maize yield in Jilin Province, while waterlogging showed a positive correlation. The relative importance results show that drought has a greater impact on maize yields than waterlogging, and the impacts of drought and waterlogging events on maize yields are mainly concentrated in the middle and late growth periods. The findings could inform the development of contingency plans for farmers to minimize crop losses and ensure food security in the region.

The paper investigates compound flooding from waves, sea surge and river flow in northern Jakarta, Indonesia, which is a global hotspot of flooding, by combining process-based coastal and river models. The coastal hydrodynamic modelling of Jakarta Bay in Indonesia shows that coastal storms can lead to a substantial increase in sea water level due to wind and wave setup in the nearshore areas, including Muara Angke river inlet. The compound flood hazard from a range of flood scenarios was simulated and analysed. The results reveal that low-lying areas around the river inlet are prone to flooding even during regular, low-intensity storm events, while rarer storms caused extensive floods. Floods were not caused by direct overwashing of sea defences but by overspill of the banks of the river inlet due to high sea water level caused by wind set up, wave setup, and sea surge obstructing the drainage of the river and elevating its water level during storms. We also found that the sea level rise combined with rapid land subsidence will inundate the existing coastal flood defences during storms in future. The majority of the city will be below mean sea level by 2100. The overflow of existing coastal defences will lead to extensive flooding in northern, western, and eastern Jakarta unless the defences are upgraded to keep up with future sea level rise.

The determination of seismic risk in urban settlements has received increasing attention in the scientific community during the last decades since it allows to identify the most vulnerable portions of urban areas and therefore to plan appropriate strategies for seismic risk reduction. In order to accurately evaluate the seismic risk of urban settlements it should be necessary to estimate in detail the seismic vulnerability of all the existing buildings in the considered area. This task could be very cumbersome due to both the great number of information needed to accurately characterize each building and the huge related computational effort. Several simplified methods for the assessment of the seismic vulnerability of existing buildings have been therefore presented in the literature. In order to estimate the occurrence of damage in buildings due to possible seismic phenomena, the published studies usually refer to response spectra evaluated according to seismic events expected in the territory with assumed probabilities. In the present paper seismic events are instead simulated using a modified Olami–Feder–Christensen (OFC) model, within the framework of self-organized criticality. The proposed methodology takes into account some geological parameters in the evaluation of the seismic intensities perceived by each single building, extending the approach presented in a previous study of some of the authors. Here, a large territory in the Sicilian oriental coast, the metropolitan area of Catania, which includes several urbanized zones with different features, has been considered as a new case study. Applications of the procedure are presented first with reference to seismic sequences of variable intensity, whose occurrence is rather frequent in seismic territories, showing that the damage can be progressively accumulated in the buildings and may lead to their collapse even when the intensities of each single event are moderate. Moreover, statistically significant simulations of single major seismic events, equivalent to a given sequence in terms of produced damages on buildings, are also performed. The latter match well with a novel a-priori risk index, introduced with the aim of characterizing the seismic risk of each single municipality in the considered metropolitan area. The proposed procedure can be applied to any large urbanized territory and, allowing to identify the most vulnerable areas, can represent a useful tool to prioritize the allocation of funds. This could be a novelty for risk policies in many countries in which public subsidies are currently assigned on a case-by-case basis, taking into account only hazard and vulnerability. The use of an a-priori risk index in the allocation process will allow to take into due account the relevant role of exposure.

This article presents the process followed by UN-SPIDER when contributing to the uptake of scientific results by technical staff in government agencies and people in communities at risk. The article suggests the chain of four types of actors to describe the process by which scientific outputs are incorporated by end-users in the topic of disaster management. It makes reference to scientific liaisons as those actors that facilitate the uptake of scientific results by transforming such results into more usable products. In addition, it outlines the role of technical staff in government agencies who take scientific results and products to generate relevant policies, norms and regulations that should be used by those at risk to minimize the potential impact of natural or technological hazards based on the notions of risks.

Artificial levees along alluvial rivers are major components of flood-risk mitigation. This is especially true in the case of Hungary, where more than one-third of the country is threatened by floods and protected by an over 4200-km-long levee system. Most of such levees were built in the nineteenth century. Since then, several natural and anthropogenic processes, such as compaction and erosion, might have contributed to these earth structures' slow but steady deformation. Meanwhile, as relevant construction works were scarcely documented, the structure and composition of artificial levees are not well known. Therefore, the present analysis mapped structural differences, possible compositional deficiencies, and sections where elevation decrease is significant along a 40-km section of the Lower Tisza River. Investigations were conducted using real-time kinematic GPS and ground-penetrating radar (GPR). Onsite data acquisition was complemented with an analysis using a Persistent Scatterer Synthetic Aperture Radar to assess general surface deformation. GPR profiles showed several anomalies, including structural and compositional discontinuities and local features. The GPR penetration depth varied between 3 and 4 m. According to height measurements, the mean elevation of the levee crown decreased by 8 cm in 40 years. However, the elevation decrease reached up to 30 cm at some locations. Sections affected by structural anomalies, compositional changes, and increased surface subsidence are especially sensitive to floods when measurement results are compared with flood phenomena archives.

The agreement between meteorological data and societal perception is essential in supporting a robust policy making and its implementation. In humid tropic watersheds like Brantas, such consensus is important for water resources management and policies. This study exemplifies an effort to understand the long-term rainfall characteristics within the watershed and to build a common link among the differing data sources: CHIRPS rainfall satellite data, rain gauge data, and farmers perceptions. Six rainfall characteristics were derived using statistical measures from the scientific data and then were translated to a series of structured questionnaires given to small-scale farmers. A consensus matrix was built to examine the level of agreement among three data sources, supporting the spatial pattern of the meteorological data and farmers perception. Two rainfall attributes were classified with high agreement, four with moderate and one with low agreement. The agreements and discrepancies of rainfall characteristics were found in the study area. The discrepancies originated from the accuracy in translating scientific measurements to practical meanings for farmers, complexity of the farming system, the nature of phenomena in questions, and farmers’ ability to record long-term climatic events. This study shows an implication that a combined approach to link scientific data and societal data is needed to support powerful climate policy making.

The study of the morphological characteristics of native species that are present in shallow landslide prone areas is an important factor for the selection of species to be used for soil conservation through soil bioengineering techniques. Previous research has assessed hydrological and mechanical effects of plant species; however, an overview of the morphological parameters of plant species focused on soil bioengineering is still lacking, this is crucial, especially when a long-term slope restoration system is required. Therefore, the aim of this research is to quantify the above ground (plant height, plant mass) and below ground (root diameter, root length and root number) morphological characteristics and to establish the relations with the appropriateness of a plant species as a soil bioengineering element. In total, 122 plant specimens distributed in 5 families, 6 genus and 5 species were registered and measured from the study area. Results showed that Boehmeria spicata could be a key species for the soil bioengineering, which morphology was found as follows: plant height of plant (15 ± 6.09 cm); root length (7.58 ± 5.58 cm); plant mass (1.65 ± 1.3 g); root diameter (1.48 ± 0.58 cm); root number (7 ± 2) and pull-out force (28.98 ± 13.96 N), showing a higher inclination to the canonical axe of number of roots, which resulted the variable with highest correlation with pull-out force.

Karkheh basin is a flood-prone region in Iran that was severely affected by devastating river floods in 2019. This study addressed some of the factors that affected on this event and were emphasized in the government inquiry committee. These include strategic questions such as the effect of prior precipitation in the basin and how the dams could mitigate the floods peak and volume. These questions as well as deficits in the rainfall data led to the application of the Soil and Water Assessment Tool model and the global land data assimilation system rainfall data to address the research questions. The results showed the initial managements of the dams prior to the occurrence of these floods was definitely affected by a decade continuous drought in the basin and the concerns about its continuation in 2019. The events occurred during March and April 2019, however, the precipitation occurred prior on October 2018 along with the resulted snowpack and soil saturation played a significant role in intensifying the floods. Although there were some limitations for the full operation of Seymareh Dam, the decision regarding its full operation could reduce the peak inflow to the Karkheh Dam from 8529 to 5447 m³/s. Finally, it is crucial to provide more accurate prediction systems, undertaking rapid and flexible responses and do not be misled by continuous droughts. Graphic Abstract Flowchart of the databases and models to address the research questions

This paper presents a methodology to evaluate life safety risk of coastal communities vulnerable to seismic and tsunami hazards. The work explicitly incorporates two important aspects in tsunami evacuation modeling: (1) the effect of earthquake-induced damage to buildings on building egress time, (2) the effect of earthquake-induced debris on horizontal evacuation time. The city of Seaside, Oregon, is selected as a testbed community. The hazard is based on a megathrust earthquake and tsunami from the Cascadia Subduction Zone that was defined in a previous study. The built environment consists of buildings and the transportation network for the city. Fragility analysis is used to estimate the seismic damage to buildings and resulting debris that covers portions of the road network. The horizontal evacuation time is determined based on the shortest path to shelters, including the increased travel time due to the earthquake-generated debris. The effects of different mitigation strategies are quantified. Results indicate the fatality and life safety risk of a near-field tsunami increases by 4.2–8.3 times when the effects of building egress and earthquake-induced debris are considered. The choice of population layer affects the life safety risk and thus the maximum risk is obtained when daytime populations are considered. Use of mitigation strategies result in a significant decrease in the number of fatalities. For hazards with recurrence intervals larger than 500- to 1000-years, the seismic retrofit is comparable to vertical evacuation and an effective strategy in reducing fatalities and associated risks. Implementing all mitigation strategies reduces the life safety risk by 90%.

The cementitious behavior of Rice Husk Ash (RHA) has caused its possible addition as a replacement material for cement which has been proven to influence the strength of concrete. In this study, Machine Learning (ML) algorithms have been used to predict the compressive strength of RHA-based concrete in a shorter period without any errors. In this regard, six different ML techniques, i.e., Linear Regression, Decision Tree, Gradient Boost, Artificial Neural Network, Random Forest and Support Vector Machines, have been employed to predict the compressive strength using twelve input features and 462 data points. The performances of models have been checked using errors, Pearson correlation coefficient (R²), Taylor’s diagram, box plots and Sensitivity analysis. The outcome of this study indicated that the Decision Tree, Gradient Boost, and Random Forest models had provided better results (R² > 0.92) than the other algorithms in terms of minimal errors and high accuracy in predicting compressive strength. The sensitivity analysis indicated that the specific gravity of RHA and water–cement ratio significantly (more than 95%) impact the compressive strength of the RHA-based concrete in contrast to the other parameters.

Among the land degradation processes, gully erosion is the one that poses more environmental and societal challenges in arid regions. Predicting spatiotemporal gully development in a region under changing conditions is important to adopt proper mitigation measures. Here we investigate the Ghapan-Olya watershed in Golestan province in Iran, which is affected by many erosional landforms, including gully phenomena. We apply the pixel-based distributed LANDPLANER model to predict where rainfall induced gullies will occur by exploiting input maps including UAV data, the region soil, and seasonal land use information. We compare our topographic thresholds and an erosion index with field observations through the application of quantitative metrics such as sensitivity, specificity, fallout, precision, and recall. Our study reveals that the spatial density of the gully’s location is more frequently predicted in the areas with an altitude of about 200–300 m, steep slope (between 15 and 30 degrees), and low average accumulation value (< 100) in the southeast facing slope. We obtain higher values of erosion index and topographic threshold for the minimum curve number where intense rainfalls are more frequent and where land use and cover conditions are more predisposing for gully occurrence. We obtain the largest values of soil erosion indices in the fall scenario when the daily rainfall is 80 mm (6.27), followed by the summer scenario with 80 mm daily rainfall (4.88), and spring again with 80 mm daily rainfall (2.99). In addition, topographic threshold maps illustrate the largest amount of soil erosion for the curve number scenario (without considering daily rainfall) in autumn. Our approach allows simulating gully erosion under changing conditions.

Assessing the destruction caused by a tsunami is a challenging task that must be completed quickly with limited resources and information. To address this issue, we propose a method for accurate damage mapping using binary classification of high-resolution satellite imagery, where we enhance the performance of three pre-trained deep neural network models (Vgg19, Inception, and Xception). The pre-trained models are used, which have been previously trained on large datasets, and transferred to our tsunami problem. We also develop a custom network architecture specifically designed for tsunami damage detection using high-resolution remote sensing data, improving the accuracy of automated binary classification. We investigate the impact of various parameters and learning rates to detect small objects, demonstrating the suitability of our approach for tsunami damage assessment. Our network outperforms traditional and current deep learning-based approaches, as it shows low bias and high variance datasets that result in a skillful model. Specifically, we observe that Inception-v3 performs best on the dataset, exhibiting good behavior with low errors and achieving the best overall score with 24.11 min, while other models score between 30.50 min for Vgg19 and 45.33 min for Xception. Our study focuses on two important binary classification categories, tsunami-stricken and non-stricken areas, for which we train the proposed framework on a dataset comprising 30,000 small tiles of high-resolution satellite images obtained from Mexer satellite images. The model is validated on 8000 images using the Jupyter notebook of the Anaconda deep learning framework.