Navegando por Assunto "Landslides"

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    Antecedent precipitation index to estimate soil moisture and correlate as a triggering process in the occurrence of landslides
    (Scientific Research Publishing) Moraes, Marcio Augusto Ernesto de; Mendes Filho, Walter Manoel; Mendes, Rodolfo Moreda; Bortolozo, Cassiano Antonio; Metodiev, Daniel; Andrade, Marcio Roberto Magalhães de; Egas, Harideva Marturano; Mendes, Tatiana Sussel Gonçalves; Pampuch, Luana Albertani
    Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process; thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems; hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.
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    ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition
    (Scientific Research Publishing) Bortolozo, Cassiano Antonio; Pampuch, Luana Albertani; Andrade, Marcio Roberto Magalhães de; Metodiev, Daniel; Carvalho, Adenilson Roberto; Mendes, Tatiana Sussel Gonçalves; Pryer, Tristan; Egas, Harideva Marturano; Mendes, Rodolfo Moreda; Sousa, Isadora Araújo; Power, Jenny
    A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
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    Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
    (European Geosciences Union) Alcântara, Enner; Marengo, José Antonio; Mantovani, José; Londe, Luciana de Resende; San, Rachel Lau Yu; Park, Edward; Lin, Yunung Nina; Wang, Jingyu; Mendes, Tatiana; Cunha, Ana Paula; Pampuch, Luana; Seluchi, Marcelo; Simões, Silvio; Cuartas, Luz Adriana; Goncalves, Demerval; Massi, Klécia; Alvalá, Regina; Moraes, Osvaldo; Souza Filho, Carlos; Mendes, Rodolfo Moreda; Nobre, Carlos
    On 15 February 2022, the city of Petrópolis in the highlands of the state of Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm), generated by a strongly invigorated mesoscale convective system. It resulted in flash floods and subsequent landslides that caused the deadliest landslide disaster recorded in Petrópolis, with 231 fatalities. In this paper, we analyzed the root causes and the key triggering factors of this landslide disaster by assessing the spatial relationship of landslide occurrence with various environmental factors. Rainfall data were retrieved from 1977 to 2022 (a combination of ground weather stations and the Climate Hazards Group InfraRed Precipitation – CHIRPS). Remotely sensed data were used to map the landslide scars, soil moisture, terrain attributes, line-of-sight displacement (land surface deformation), and urban sprawling (1985–2020). The results showed that the average monthly rainfall for February 2022 was 200 mm, the heaviest recorded in Petrópolis since 1932. Heavy rainfall was also recorded mostly in regions where the landslide occurred, according to analyses of the rainfall spatial distribution. As for terrain, 23 % of slopes between 45–60∘ had landslide occurrences and east-facing slopes appeared to be the most conducive for landslides as they recorded landslide occurrences of about 9 % to 11 %. Regarding the soil moisture, higher variability was found in the lower altitude (842 m) where the residential area is concentrated. Based on our land deformation assessment, the area is geologically stable, and the landslide occurred only in the thin layer at the surface. Out of the 1700 buildings found in the region of interest, 1021 are on the slope between 20 to 45∘ and about 60 houses were directly affected by the landslides. As such, we conclude that the heavy rainfall was not the only cause responsible for the catastrophic event of 15 February 2022; a combination of unplanned urban growth on slopes between 45–60∘, removal of vegetation, and the absence of inspection were also expressive driving forces of this disaster.
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    Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil
    (Elsevier) Sousa, Isadora Araújo; Bortolozo, Cassiano Antonio; Mendes, Tatiana Sussel Gonçalves; Andrade, Marcio Roberto Magalhães de; Dolif Neto, Giovanni; Metodiev, Daniel; Pryer, Tristan; Howley, Noel; Simões, Silvio Jorge Coelho; Mendes, Rodolfo Moreda
    Climate change has increased the frequency of extreme weather events and, consequently, the number of occurrences of natural disasters. In Brazil, among these disasters, floods, flash floods, and landslides account for the highest number of deaths, the latter being the most lethal. Bearing in mind the importance of monitoring areas susceptible to disasters, the REMADEN/REDEGEO project of the National Center for Monitoring and Natural Disaster Alerts (Cemaden) has promoted the installation of a network of soil moisture sensors in regions with a long history of landslides. This network was used in the present paper as a base to develop a system for moisture forecasting in those critical zones. The time series of rainfall and moisture were used in an inversion algorithm to obtain the geotechnical parameters of the soil. Then the geotechnical model was used in a forward calculation with the rainfall prediction to obtain the soil moisture forecast. The landslide events of March 2020 and May 2022 in Guarujá and Recife, respectively, were used as study cases for the developed system. The obtained results indicate that the proposed methodology has the potential to be used as an important tool in the decision-making process for issuing landslide alerts.
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    Socio-geoenvironmental vulnerability index (SGeoVI) derived from hybrid modeling related to populations at-risk to landslides
    (Springer Nature) Ávila, Frederico Fernandes de; Alvalá, Regina Célia dos Santos; Mendes, Rodolfo Moreda; Amore, Diogo de Jesus
    In the present study, we propose a transdisciplinary investigation aimed at developing an index to assess the vulnerability to landslides in the Brazilian municipality of São José dos Campos. The proposed Socio-Geoenvironmental Vulnerability Index (SGeoVI) was developed using a hybrid modeling approach that integrates socioeconomic data with landslide susceptibility mapping. Landslide susceptibility was derived from the FS FIORI deterministic model, which calculates the Safety Factor (FS) using the concept of limit equilibrium. The mapping of land use and land cover enabled the inclusion of new parameters related to certain anthropogenic conditions and vegetation cover, such as the overabundance of buildings and the presence or absence of tree vegetation, which are significant factors influencing landslide occurrence. Socioeconomic indicators were extracted from Brazilian Institute of Geography and Statistics (IBGE) census data, and variables were selected and spatially represented for each of São José dos Campos’s 1073 census tracts. Socioeconomic indicators were taken into account for the SGeoVI proposal and categorized into five thematic groups: economic; educational; housing infrastructure; social dependency; e family structure. Based on the Socio-geoenvironmental vulnerability indicators, several neighborhoods in São José dos Campos, SP, were selected for a detailed SGeoVI assessment. In Pinheirinhos, a neighborhood situated in the southernmost part of São José dos Campo, the highest SGeoVI value reached to 0.91. In contrast, Jardim Apolo I, an established neighborhood located in the Central Region of São José dos Campos, exhibited a considerably lower SGeoVI value of only 0.04, for instance. The verification results of SGeoVI revealed socioeconomic disparities that align with geomorphological variations. The northern and southern parts of the municipality exhibit high susceptibility to landslides, coupled with low-income communities and inadequate housing structures, contributing to elevated socio-geoenvironmental vulnerability. In contrast, central regions feature flat terrain, reduced natural susceptibility, higher income levels, and improved access to housing infrastructure, resulting in lower vulnerability values. Given its ability to assess intra-municipal socio-geoenvironmental vulnerability, SGeoVI holds potential for extrapolation to other municipalities. Consequently, it can serve as a valuable tool for municipal authorities in formulating public policies aimed at landslide risk management.