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Land
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Landslide and Natural Hazard Monitoring
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Landslide and Natural Hazard Monitoring

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 30388

Special Issue Editors

Prof. Dr. Domenico Calcaterra

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Guest Editor
Federico II University of Naples, Department of Earth, Environmental and Resource Sciences, Monte Sant Angelo Campus, 80126 Napoli, Italy
Interests: landslides; floods; sinkholes; remote sensing; risk analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Diego Di Martire

E-Mail Website
Guest Editor
Department of Earth Sciences, Environment and Resources, University of Naples Federico II, 80126 Napoli, Italy
Interests: landslides, hazard and risk assessment; interferometry SAR; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Guerriero

E-Mail Website
Guest Editor
Department of Earth Sciences, Environment and Resources, University of Naples Federico II, Via Cintia 21, University Campus of Monte S. Angelo, Bldg 10, 80126 Napoli, Italy
Interests: landslides; floods; sinkholes; remote sensing; sensors; terrestrial laser scanning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Roberto Tomás

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing global impact of landslides and natural hazards (sinkholes, snow avalanches, floods, glaciers, etc.), also connected to population growth and the ongoing climate change, is emphasizing the role of Earth-surface monitoring from a risk assessment perspective. In the last two decades, new satellite- and ground-based approaches have revolutionized Earth-surface monitoring capabilities in terms of both time/space resolution and early-warning abilities. From the launch of the first satellite in 1957 to the recently available nanosatellite constellations, mid- to high-frequency optical, multispectral, and radar images of the Earth surface have provided support to the localization and tracking of surface processes and deformation related to landslides and chains of geologic hazards at different scales (national to local). At the same time, ground-based remote sensing instruments and techniques like ground-based interferometric synthetic aperture radar (GB-SAR), terrestrial laser scanners (TLSs), and photogrammetry/object-tracking algorithms, as well as airborne techniques (e.g., UAV), have provided a new way to catch initial surface deformations and eventual movements in slope processes involving landslides, snow avalanches, glaciers, etc. Great effort has also been made to advance the capabilities of sensors and sensor networks in capturing the geodetic and hydrologic signals of potentially hazardous natural processes. Many systems, including low-cost systems, have been developed and successfully applied for geologic risk mitigation through threshold-based early-warning approaches.

On this basis, we are running a Special Issue dealing with recent advancements in landslide and natural hazard monitoring using satellite remote sensing, ground-based remote sensing, sensor-network based, and integrated methods, in the last case also including conventional ground-based techniques. We welcome contributions regarding recent and newly developed monitoring instruments, methods, techniques, and approaches, as well as relevant case histories and current and near-future perspectives in monitoring-based early warning. Landslides, sinkholes, snow avalanches, glaciers, floods, and their cascading combinations are examples of natural hazardous processes of interest to this Special Issue.

Prof. Dr. Domenico Calcaterra
Dr. Diego Di Martire
Dr. Luigi Guerriero
Prof. Dr. Roberto Tomás
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Natural hazards
  • Landslides
  • Satellite monitoring
  • Ground-based monitoring
  • UAV-based monitoring
  • Sensors
  • Early warning

Published Papers (11 papers)

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Research

28 pages, 5195 KiB  
Article
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
by Esteban Bravo-López, Tomás Fernández Del Castillo, Chester Sellers and Jorge Delgado-García
Land 2023, 12(6), 1135; https://doi.org/10.3390/land12061135 - 27 May 2023
Cited by 6 | Viewed by 1698
Abstract
Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order [...] Read more.
Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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17 pages, 7363 KiB  
Article
Using Historical Aerial Photography in Landslide Monitoring: Umka Case Study, Serbia
by Dejan Radovan Đorđević, Uroš Đurić, Saša Tomislav Bakrač, Siniša Milanko Drobnjak and Stevan Radojčić
Land 2022, 11(12), 2282; https://doi.org/10.3390/land11122282 - 13 Dec 2022
Cited by 4 | Viewed by 1688
Abstract
The application of remote sensing methods provides useful information that can be used for numerous research. Thus, spatial changes in soil, vegetation, hydrography and such can be analyzed. By analyzing the data obtained by remote sensing methods, high-quality and important data can be [...] Read more.
The application of remote sensing methods provides useful information that can be used for numerous research. Thus, spatial changes in soil, vegetation, hydrography and such can be analyzed. By analyzing the data obtained by remote sensing methods, high-quality and important data can be obtained for monitoring changes in soil movement caused by landslides. This method provides the possibility of determining the state of the observed space over a longer period of time. Historical aerial imagery has a high level of spatial detail analysis. Comparative analysis of the aerial imagery from the past, recent ones and other surveys can certainly provide information on the trend of ground movement, as well as lead to conclusions for taking specific measures. The present paper gives an example of the analysis of the particular area of the “Umka” landslide based on historical surveys. The “Umka” landslide is located along the right bank of the Sava River near the city of Belgrade, which, with its long-term activity, jeopardizes residential buildings, infrastructure facilities and the population that still lives on it. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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21 pages, 7738 KiB  
Article
Assessment of Land Deformation and the Associated Causes along a Rapidly Developing Himalayan Foothill Region Using Multi-Temporal Sentinel-1 SAR Datasets
by Shubham Awasthi, Divyesh Varade, Sutapa Bhattacharjee, Hemant Singh, Sana Shahab and Kamal Jain
Land 2022, 11(11), 2009; https://doi.org/10.3390/land11112009 - 10 Nov 2022
Cited by 6 | Viewed by 2511
Abstract
Land deformation has become a crucial threat in recent decades, caused by various natural and anthropogenic activities in the environment. The seismic land dynamics, landslides activities, heavy rainfall resulting in flood events, and subsurface aquifer shrinkage due to the excessive extraction of groundwater [...] Read more.
Land deformation has become a crucial threat in recent decades, caused by various natural and anthropogenic activities in the environment. The seismic land dynamics, landslides activities, heavy rainfall resulting in flood events, and subsurface aquifer shrinkage due to the excessive extraction of groundwater are among the major reasons for land deformation, which may cause serious damage to the overall land surface, civil infrastructure, underground tunnels, and pipelines, etc. This study focuses on preparing a framework for estimating land deformation and analyzing the causes associated with land deformation. A time-series SAR Interferometry-based technique called PsInSAR was used to measure land deformation, using Sentinel-1 datasets from 2015 to 2021 by estimating land deformation velocities for this region. The obtained PSInSAR deformation velocity results ranged between −4 mm to +2 mm per year. Further, land use land cover (LULC) changes in the area were analyzed as an essential indicator and probable cause of land deformation. LULC products were first generated using Landsat-8 images for two time periods (2015, 2021), which were then evaluated in accordance with the deformation analysis. The results indicated an increase in the built-up areas and agricultural cover in the region at the cost of shrinkage in the vegetated lands, which are highly correlated with the land subsidence in the region, probably due to the over-extraction of groundwater. Further, the outer region of the study area consisting of undulating terrain and steep slopes also coincides with the estimated high subsidence zones, which could be related to higher instances of landslides identified in those areas from various primary and secondary information collected. One of the causes of landslides and soil erosion in the region is identified to be high-level precipitation events that loosen the surface soil that flows through the steep slopes. Furthermore, the study region lying in a high seismic zone with characteristic unstable slopes are more susceptible to land deformation due to high seismic activities. The approach developed in the study could be an useful tool for constant monitoring and estimation of land deformation and analysis of the associated causes which can be easily applied to any other region. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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27 pages, 20708 KiB  
Article
Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India
by Abhik Saha, Vasanta Govind Kumar Villuri and Ashutosh Bhardwaj
Land 2022, 11(10), 1711; https://doi.org/10.3390/land11101711 - 02 Oct 2022
Cited by 13 | Viewed by 2313
Abstract
Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on [...] Read more.
Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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19 pages, 5136 KiB  
Article
Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent
by Deqiang Cheng and Chunliu Gao
Land 2022, 11(9), 1519; https://doi.org/10.3390/land11091519 - 09 Sep 2022
Cited by 1 | Viewed by 1356
Abstract
Carrying out mountain-hazards developing environment research is helpful for understanding the spatial characteristics of the mountain hazards so as to contribute to mountain-hazards prevention and mitigation and the safety of infrastructures and major projects. In this study, the Eurasian continent was selected as [...] Read more.
Carrying out mountain-hazards developing environment research is helpful for understanding the spatial characteristics of the mountain hazards so as to contribute to mountain-hazards prevention and mitigation and the safety of infrastructures and major projects. In this study, the Eurasian continent was selected as the research area to conduct regionalization research on mountain-hazards developing environments. Using peak ground acceleration (PGA), the annual average precipitation and topographic relief as root factors of mountain-hazards developing environments (known as PPR factors) to represent the characteristics of geological structures, climatic impacts and geomorphology, the regionalization of mountain-hazards developing environments of the Eurasian continent was conducted through the combination of computer automatic classification and later artificial cartographic generalization. Finally, 15 subregions were obtained. A preliminary judgment of the mountain-hazards susceptibility for each region according to the characteristics of PPR factors was made, and nine subregions were identified as the overall high-susceptibility areas of mountain hazards. Based on the analysis of the characteristics of PPR factors and the mountain-hazards susceptibility characteristics in different mountain-hazards developing environment subregions, the high susceptibility regions of mountain hazards could be divided into three types: arid and active-geologic regions, humid and active-geologic regions, and humid and inactive-geologic regions. We hope that our research provides support for subsequent works of more specific and reasonable mountain-hazards susceptibility, hazard and risk models construction for different types of mountain-hazards developing environments. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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19 pages, 12610 KiB  
Article
A Spatio-Temporal Monitoring Method Based on Multi-Source Remote Sensing Data Applied to the Case of the Temi Landslide
by Hua Wang, Qing Guo, Xiaoqing Ge and Lianzi Tong
Land 2022, 11(8), 1367; https://doi.org/10.3390/land11081367 - 21 Aug 2022
Cited by 4 | Viewed by 1733
Abstract
It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources [...] Read more.
It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources is proposed. We focus on the changes in three aspects consisting of the vegetation condition, the surface deformation information and the landslide susceptibility. Firstly, the fractional vegetation cover of the landslide is extracted from optical remote sensing Gaofen-1 (GF-1) images using the dimidiate pixel model. Next, the surface deformation information of the landslide is derived from SAR remote sensing Sentinel-1A images applying the SBAS-InSAR method. Then, the landslide susceptibility based on GF-1, Sentinel-1A images and DEM data is computed using the analytic hierarchy process method. Finally, the spatio-temporal correlations of the vegetation condition, the surface deformation information and the landslide susceptibility are compared and interpreted. The Temi landslide is located along the Jinsha River and poses a high risk of blocking the river. Taking the Temi landslide as the study area, it is indicated from the results that the fractional vegetation cover, surface deformation information and landslide susceptibility reveal a consistency in the patterns of changes in spatial and temporal terms. As the surface deformation information improves, the status of the landslide vegetation also deteriorates and the landslide susceptibility becomes high, which indicates an increased probability of the creep and even the occurrence of landslides. In contrast, when the surface deformation information drops, the vegetation condition of the landslide becomes superior and the landslide becomes less susceptible, which means the likelihood of sliding declines. This study provides a new idea for a landslide monitoring method and potential way for natural disaster prevention and mitigation. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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21 pages, 5117 KiB  
Article
Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia
by Siti Norsakinah Selamat, Nuriah Abd Majid, Mohd Raihan Taha and Ashraf Osman
Land 2022, 11(6), 833; https://doi.org/10.3390/land11060833 - 02 Jun 2022
Cited by 20 | Viewed by 3659
Abstract
Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their [...] Read more.
Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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24 pages, 6380 KiB  
Article
Avalanche Hazard Modelling within the Kráľova Hoľa Area in the Low Tatra Mountains in Slovakia
by Vladislava Košová, Mário Molokáč, Vladimír Čech and Miloš Jesenský
Land 2022, 11(6), 766; https://doi.org/10.3390/land11060766 - 24 May 2022
Cited by 4 | Viewed by 2128
Abstract
The aim of this work is a comprehensive assessment of the avalanche risk within the Kráľova hoľa area in the Low Tatra Mountains in Slovakia by the modeling of trigger areas, and the simulation of avalanche movements and their maximum impact using GIS [...] Read more.
The aim of this work is a comprehensive assessment of the avalanche risk within the Kráľova hoľa area in the Low Tatra Mountains in Slovakia by the modeling of trigger areas, and the simulation of avalanche movements and their maximum impact using GIS and the RAMMS simulation model. Within the environment of geographic information systems, we created a layer of trigger areas using a digital elevation model and a vector layer of a land cover as input data. This layer was added together with the digital elevation model to the RAMMS simulation model, where cartographic outputs were created, focusing on snow cover height, avalanche flow speed, and pressure exerted by a falling avalanche. Based on these documents, we were able to develop an updated map of the avalanche cadastre of the examined area. In the given territory, we mapped a range of trigger areas within an area of 2.6 km2 and the total range of avalanche run-outs within 14 interconnected areas. Of all the high mountains in Slovakia endangered by avalanches, this is the lowest range. The results are a suitable basis for the proper management and optimal use of the territory, which is part of Low Tatras National Park. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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33 pages, 13835 KiB  
Article
Flood Assessment and Identification of Emergency Evacuation Routes in Seti River Basin, Nepal
by Bhabana Thapa, Teiji Watanabe and Dhananjay Regmi
Land 2022, 11(1), 82; https://doi.org/10.3390/land11010082 - 05 Jan 2022
Cited by 11 | Viewed by 4631
Abstract
Sudden floods frequently occur in the Himalayas under changing climates. Rapid glacial melt has resulted in the formation of glacial lakes and associated hazards. This research aimed to (1) identify flood-prone houses, (2) determine pedestrian emergency evacuation routes, and (3) analyze their relationships [...] Read more.
Sudden floods frequently occur in the Himalayas under changing climates. Rapid glacial melt has resulted in the formation of glacial lakes and associated hazards. This research aimed to (1) identify flood-prone houses, (2) determine pedestrian emergency evacuation routes, and (3) analyze their relationships to socioeconomic status in the Seti River Basin. Detailed hazard maps were created using field survey results from unmanned aerial vehicle photogrammetry and the Hydrologic Engineering Center River Analysis System. Questionnaire, focus-group, and key-informant surveys helped identify the socioeconomic situation. Inundation maps revealed that most residents are exposed to future flooding hazards without proper evacuation routes. Highly impoverished and immigrant households were at the highest risk in terms of income inequality and migration rate (p < 0.001) and were located on the riverside. The locations of 455 laborers’ houses were significantly correlated with inundation hazards (p < 0.001). Governmental and associated agencies must develop adequate plans to relocate low-income households. Group discussions revealed the need for stronger adaptive capacity-building strategies for future risk management. Pokhara requires better systematic and scientific land-use planning strategies to address this issue efficiently. A similar approach that combines flood modeling, proper evacuation route access, and socioeconomic survey is suggested for this river basin. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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20 pages, 5127 KiB  
Article
Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece
by Spyridon Lainas, Nikolaos Depountis and Nikolaos Sabatakakis
Land 2021, 10(8), 877; https://doi.org/10.3390/land10080877 - 20 Aug 2021
Cited by 9 | Viewed by 2934
Abstract
A new methodology for shallow landslide forecasting in wildfire burned areas is proposed by estimating the annual probability of rainfall threshold exceedance. For this purpose, extensive geological fieldwork was carried out in 122 landslides, which have been periodically activated in Western Greece, after [...] Read more.
A new methodology for shallow landslide forecasting in wildfire burned areas is proposed by estimating the annual probability of rainfall threshold exceedance. For this purpose, extensive geological fieldwork was carried out in 122 landslides, which have been periodically activated in Western Greece, after the devastating wildfires that occurred in August 2007 and burned large areas in several parts of Western Greece. In addition, daily rainfall data covering more than 40 years has been collected and statistically processed to estimate the exceedance probability of the rainfall threshold above which these landslides are activated. The objectives of this study are to quantify the magnitude and duration of rainfall above which landslides in burned areas are activated, as well as to introduce a novel methodology on rainfall-induced landslide forecasting. It has been concluded that rainfall-induced landslide annual exceedance probability in the burned areas is higher when cumulative rainfall duration ranges from 6 to 9 days with local differences due to the prevailing geological conditions and landscape characteristics. The proposed methodology can be used as a basis for landslide forecasting in wildfire-affected areas, especially when triggered by rainfall, and can be further developed as a tool for preliminary landslide hazard assessment. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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14 pages, 16181 KiB  
Article
Influence of Carbonate-Flysch Contact and Groundwater Dynamics on the Occurrence of Geohazards in Istria, Croatia
by Sanja Dugonjić Jovančević, Josip Rubinić, Igor Ružić and Maja Radišić
Land 2021, 10(5), 441; https://doi.org/10.3390/land10050441 - 21 Apr 2021
Cited by 2 | Viewed by 2103
Abstract
This research focuses on the analysis of soil-water interaction at the carbonate-flysch contact on the Istrian peninsula in Croatia. As a result of the interaction of surface and groundwater and the position of flysch and carbonate rocks in the geotechnical profile, two problems [...] Read more.
This research focuses on the analysis of soil-water interaction at the carbonate-flysch contact on the Istrian peninsula in Croatia. As a result of the interaction of surface and groundwater and the position of flysch and carbonate rocks in the geotechnical profile, two problems occur in the study area: numerous instabilities and the occasionally high turbidity of drinking water. As an example, the St. Ivan spring was considered. The paper presents a complex mechanism of groundwater circulation in geological structures at carbonate-flysch contacts, differences in runoff through karst aquifers and flysch rocks during heavy rainfall under current and predicted (climate change) conditions, and the mentioned geohazards as a result of extreme precipitation. The analyses carried out showed the decisive influence of the existing geological structure on the dynamics of infiltration and precipitation runoff, as well as the risks of pronounced spring water turbidity and instability events. The main drivers of these geohazards are continuous long-term precipitation for landslides and intense daily precipitation for turbidity. Possible consequences of climate change are the increase in precipitation intensity, amount and higher variation, which subsequently brings risks such as the increase in maximum runoff, i.e., the expected more frequent occurrence of high turbidity and the more frequent occurrence of higher cumulative precipitation triggering instabilities in the area. Full article
(This article belongs to the Special Issue Landslide and Natural Hazard Monitoring)
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