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GeoHazards, Volume 2, Issue 4 (December 2021) – 10 articles

Cover Story (view full-size image): This paper evaluated the relationship between sea-level change and the severity of impacts on major habitat-forming seaweeds that sustain life on rocky shores. The vertical displacement of intertidal areas uplifted by the 7.8 Mw Kaikōura earthquake was assessed using LiDAR differencing analyses from the closest terrestrial surfaces coupled with controls on horizontal land movement and tilt. Seaweed cover measurements in equivalent intertidal zones found severe (>80%) losses at 9 of 10 sites and included the lowest uplift values (0.6 m). The results indicate major impacts beyond a functional threshold of one-quarter of the tidal range and considerable lag times in ecosystem recovery due to interactions with other stressors in the reassembly phase. View this paper
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12 pages, 1723 KiB  
Article
Integrated Assessment of Drought Impacts on Rural Areas: The Case of the Chapada Diamantina Region in Brazil
by Rodrigo Rudge Ramos Ribeiro, Samia Nascimento Sulaiman, Stefan Sieber, Miguel Angel Trejo-Rangel and Juliana Fionda Campos
GeoHazards 2021, 2(4), 442-453; https://doi.org/10.3390/geohazards2040025 - 20 Dec 2021
Cited by 2 | Viewed by 3086
Abstract
Drought is one of the most significant hazards that farmers face in rural areas. This study aims to examine an integrated assessment of the drought impacts in rural territories, considering the social perceptions related to the effects of natural hazards on health, social [...] Read more.
Drought is one of the most significant hazards that farmers face in rural areas. This study aims to examine an integrated assessment of the drought impacts in rural territories, considering the social perceptions related to the effects of natural hazards on health, social relations, income, and other impacts. The study area is located in the rural area of the Chapada Diamantina region in Northern Brazil. The characterization of the region was carried out based on historical meteorological and agricultural productivity data. The method used in this study was based on a survey of social perceptions regarding drought impacts by small rural producers through a participatory process. The results indicated how extreme events such as drought influence rural areas. In addition to agricultural productivity (~50%), aspects such as social migration and health problems were observed. Full article
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<p>The study area of Bahia in Brazil (Source: prepared by the authors).</p>
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<p>Precipitation (mm) data between 1974 and 2020 (Source: prepared by the authors based on data from ANA).</p>
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<p>Annual days without rain between 1974 and 2020 (Source: prepared by the authors based on data from ANA).</p>
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<p>Rural productivity (ton/ha) data between 1974 and 2019 (Source: prepared by the authors based on data from IBGE).</p>
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<p>Research model aspects adopted in the study (Source: prepared by the authors).</p>
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<p>Main rural activities among the local farmers (Source: prepared by the authors).</p>
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<p>Perception of the phenomena that most impact production (Source: prepared by the authors).</p>
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<p>Perception of the main health impacts related to drought (Source: prepared by the authors).</p>
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<p>Perception of the main social impacts related to drought (Source: prepared by the authors).</p>
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<p>Perception of the main rural income impacts among the local farmers related to drought (Source: prepared by the authors).</p>
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<p>Perception of the other impacts related to drought (Source: prepared by the authors).</p>
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12 pages, 2909 KiB  
Technical Note
Effect of Base Conditions in One-Dimensional Numerical Simulation of Seismic Site Response: A Technical Note for Best Practice
by Gaetano Falcone, Giuseppe Naso, Federico Mori, Amerigo Mendicelli, Gianluca Acunzo, Edoardo Peronace and Massimiliano Moscatelli
GeoHazards 2021, 2(4), 430-441; https://doi.org/10.3390/geohazards2040024 - 18 Dec 2021
Cited by 5 | Viewed by 2489
Abstract
The effects induced by the choice of numerical base conditions for evaluating local seismic response are investigated in this technical note, aiming to provide guidelines for professional applications. A numerical modelling of the seismic site response is presented, assuming a one-dimensional scheme. At [...] Read more.
The effects induced by the choice of numerical base conditions for evaluating local seismic response are investigated in this technical note, aiming to provide guidelines for professional applications. A numerical modelling of the seismic site response is presented, assuming a one-dimensional scheme. At first, with reference to the case of a homogeneous soil layer overlying a half-space, two different types of numerical base conditions, named rigid and elastic, were adopted to analyse the seismic site response. Then, geological setting, physical and mechanical properties were selected from Italian case studies. In detail, the following stratigraphic successions were considered: shallow layer 1 (shear wave velocity, VS, equal to 400 m/s), layer 2 (VS equal to 600 m/s) and layer 3 (VS equal to 800 m/s). In addition, real signals were retrieved from the web site of the Italian accelerometric strong motion network. Rigid and elastic base conditions were adopted to estimate the ground motion modifications of the reference signals. The results are presented in terms of amplification factors (i.e., ratio of integral quantities referred to free-field and reference response spectra) and are compared between the adopted numerical models. Full article
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<p>Simplified seismic site response scheme.</p>
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<p>Response spectra of the reference motions in pseudo-spectral acceleration referring to: (<b>a</b>) homogeneous case studies and (<b>b</b>) heterogeneous case studies.</p>
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<p>Response spectra in pseudo-acceleration obtained by means of <span class="html-italic">rigid</span> and <span class="html-italic">elastic</span> base conditions, with reference to linear visco-elastic materials and cover deposit thickness H equal to 15 m (<b>left side</b>) and 100 m (<b>right side</b>).</p>
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<p>Error in the estimation of AFs quantified according to the Equation (2), with reference to cover deposit thickness equal to 15 m and linear visco-elastic materials. In detail, assuming V<sub>S</sub> = 20,000 m/s for the half-space, the impedance contrast results equal to 81.5, 61.1, 40.7, and 30.6 with reference to the deposit V<sub>S</sub> equal to 300, 400, 600, and 800 m/s, respectively.</p>
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<p>Error in the estimation of AFs quantified according to Equation (2), with reference to cover deposit thickness equal to 100 m and linear visco-elastic materials. In detail, assuming V<sub>S</sub> = 20,000 m/s for the half-space, the impedance contrast results equal to 81.5, 61.1, 40.7, and 30.6 with reference to deposit V<sub>S</sub> equal to 300, 400, 600, and 800 m/s, respectively.</p>
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<p>Mechanical parameters for the heterogeneous case study: (<b>a</b>) V<sub>S</sub>-z profiles, (<b>b</b>) G<sub>S</sub>(γ)/G<sub>0</sub> and D(γ) curves (briefly, NL curves), and (<b>c</b>) soil units–depth range–NL curves association.</p>
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<p>Distribution of ε<sub>AF</sub> referring to the heterogenous site profile 1 (H<sub>deposit</sub> = 30 m) and to the heterogenous site profile 2 (H<sub>deposit</sub> = 120 m).</p>
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<p>Profiles of maximum shear strain, γ<sub>max</sub>, with depth referring to the heterogenous site profile 1 (H<sub>deposit</sub> = 30 m) and to the heterogenous site profile 2 (H<sub>deposit</sub> = 120 m).</p>
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15 pages, 5726 KiB  
Article
Morphotectonic Structures along the Southwestern Margin of Lesvos Island, and Their Interrelation with the Southern Strand of the North Anatolian Fault, Aegean Sea, Greece
by Paraskevi Nomikou, Dimitris Evangelidis, Dimitrios Papanikolaou, Danai Lampridou, Dimitris Litsas, Yannis Tsaparas, Ilias Koliopanos and Maria Petroulia
GeoHazards 2021, 2(4), 415-429; https://doi.org/10.3390/geohazards2040023 - 14 Dec 2021
Cited by 1 | Viewed by 3735
Abstract
A hydrographic survey of the southwestern coastal margin of Lesvos Island (Greece) was conducted by the Naftilos vessel of the Hellenic Hydrographic Service. The results have been included in a bathymetric map and morphological slope map of the area. Based on the neotectonic [...] Read more.
A hydrographic survey of the southwestern coastal margin of Lesvos Island (Greece) was conducted by the Naftilos vessel of the Hellenic Hydrographic Service. The results have been included in a bathymetric map and morphological slope map of the area. Based on the neotectonic and seismotectonic data of the broader area, a morphotectonic map of Lesvos Island has been compiled. The main feature is the basin sub-parallel to the coast elongated Lesvos Basin, 45 km long, 10–35 km wide, and 700 m deep. The northern margin of the basin is abrupt, with morphological slopes towards the south between 35° and 45° corresponding to a WNW-ESE normal fault, in contrast with the southern margin that shows a gradual slope increase from 1° to 5° towards the north. Thus, the main Lesvos Basin represents a half-graben structure. The geometry of the main basin is interrupted at its eastern segment by an oblique NW-SE narrow channel of 650 m depth and 8 km length. East of the channel, the main basin continues as a shallow Eastern Basin. At the western part of the Lesvos margin, the shallow Western Basin forms an asymmetric tectonic graben. Thus, the Lesvos southern margin is segmented in three basins with different morphotectonic characteristics. At the northwestern margin of Lesvos, three shallow basins of 300–400 m depth are observed with WNW-ESE trending high slope margins, probably controlled by normal faults. Shallow water marine terraces representing the last low stands of the glacial periods are observed at 140 m and 200 m depth at the two edges of the Lesvos margin. A secondary E-W fault disrupts the two terraces at the eastern part of the southern Lesvos margin. The NE-SW strike-slip fault zone of Kalloni-Aghia Paraskevi, activated in 1867, borders the west of the Lesvos Basin from the shallow Western Basin. The Lesvos bathymetric data were combined with those of the eastern Skyros Basin, representing the southern strand of the North Anatolian Fault in the North Aegean Sea, and the resulted tectonic map indicates that the three Lesvos western basins are pull-aparts of the strike-slip fault zone between the Skyros Fault and the Adramytion (Edremit) Fault. The seismic activity since 2017 has shown the co-existence of normal faulting and strike-slip faulting throughout the 90 km long Lesvos southern margin. Full article
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<p>The studied area of Lesvos Island at the eastern Aegean Sea. Its location lies at the junction of the two Aegean micro-plate boundaries, represented by the Central Hellenic Shear Zone (CHSZ) and the West Anatolian Shear Zone (WASZ) (Inset Map, modified by Ref. [<a href="#B10-geohazards-02-00023" class="html-bibr">10</a>]).</p>
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<p>Bathymetric map of the Lesvos southwestern margin.</p>
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<p>(<b>a</b>) Simplified morphological map of the Lesvos southwestern margin and topographic profiles across the major structures of the area. T1 and T2 represent the submarine terraces. WB1, WB2, and WB3 represent the three western basins. WB represents the shallow Western Basin. NM and SM represent the northern and southern margin of the Lesvos Basin. EB represents the Eastern Basin. CH represents the channel. (<b>b</b>) Three topographic profiles through the Western basin (WB, P1), the main Lesvos Basin with its northern (NM) and southern (SM) margin (P2), and the Eastern Basin (EB, P3). Or.mt: Ordymnos mt. Ol.mt: Olympus mt.</p>
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<p>(<b>a</b>) Morphological slope map of the studied area. (<b>b</b>) Simplified morphotectonic map of the Lesvos southwestern margin resulted from the interpretation of the morphological slope map. (<b>c</b>) Rose diagram of the zones of slope discontinuities (24 measurements).</p>
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<p>Panoramic views of the offshore/onshore relief along the Lesvos southwestern margin. (<b>a</b>) View from the south of the southern Lesvos margin. (<b>b</b>) View from the southeast of the NW-SE channel disrupting the eastern part of the Lesvos Basin. (<b>c</b>) View from the southwest of the Terraces T1 and T2 at the eastern Lesvos margin. (<b>d</b>) View from the southeast of the Terraces T1 and T2 at the western Lesvos margin. MF: main fault of the northern margin of the Lesvos Basin, WF: Western fault of the northern margin of the Western Basin, SEF: Southeastern fault, running south of the Eastern Basin and disrupting the T1 and T2 terraces at the eastern margin of Lesvos.</p>
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<p>(<b>a</b>) Morphotectonic map of Lesvos combining offshore and onshore structures. The red stars correspond to the major shock, F1, (6.3) and the two major aftershocks (5.2 and 5.0) activated during the 2017 seismic activity. Fault plane solutions of the main shock and two major aftershocks after [<a href="#B24-geohazards-02-00023" class="html-bibr">24</a>]. S: Sigri, V: Vatera, Pl: Plomari, K: Kalloni, A.P.: Aghia Paraskevi, M: Mantamado, Ke: Keramia, My: Mytilini, Le mt: Lepetymnos mt., Ol mt: Olympus mt. (<b>b</b>) Three tectonic profiles across the Western Basin (<b>A</b>), the main Lesvos Basin (<b>B</b>), and the Eastern Basin (<b>C</b>) (see also the morphological profiles in <a href="#geohazards-02-00023-f003" class="html-fig">Figure 3</a>b). WB: Western Basin, NM: northern margin of the Lesvos Basin, SM: southern margin of the Lesvos Basin, EB: Eastern Basin, SEF: southeastern fault.</p>
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<p>(<b>a</b>) Simplified morpho-tectonic map of the Skyros Basin and related strike-slip fault zone and the Lesvos western marginal structures, based on this study and [<a href="#B26-geohazards-02-00023" class="html-bibr">26</a>]. (<b>b</b>) Tectonic sketch of the Skyros and Lesvos structures, showing the pull-apart nature of the Lesvos western basins, WB1, WB2, and WB3, in between the Skyros and Edremit strike-slip fault zones. Sk: Skyros, Le: Lesvos, Ed: Edremit. Moment Tensor solutions: <a href="https://orfeus.gein.noa.gr/gisola/realtime/2021/" target="_blank">https://orfeus.gein.noa.gr/gisola/realtime/2021/</a> (accessed on 13 December 2021).</p>
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17 pages, 32974 KiB  
Article
Potential Fault Displacement Hazard Assessment Using Stochastic Source Models: A Retrospective Evaluation for the 1999 Hector Mine Earthquake
by Katsuichiro Goda
GeoHazards 2021, 2(4), 398-414; https://doi.org/10.3390/geohazards2040022 - 4 Dec 2021
Cited by 3 | Viewed by 2929
Abstract
Surface fault displacement due to an earthquake affects buildings and infrastructure in the near-fault area significantly. Although approaches for probabilistic fault displacement hazard analysis have been developed and applied in practice, there are several limitations that prevent fault displacement hazard assessments for multiple [...] Read more.
Surface fault displacement due to an earthquake affects buildings and infrastructure in the near-fault area significantly. Although approaches for probabilistic fault displacement hazard analysis have been developed and applied in practice, there are several limitations that prevent fault displacement hazard assessments for multiple locations simultaneously in a physically consistent manner. This study proposes an alternative approach that is based on stochastic source modelling and fault displacement analysis using Okada equations. The proposed method evaluates the fault displacement hazard potential due to a fault rupture. The developed method is applied to the 1999 Hector Mine earthquake from a retrospective perspective. The stochastic-source-based fault displacement hazard analysis method successfully identifies multiple source models that predict fault displacements in close agreement with observed GPS displacement vectors and displacement offsets along the fault trace. The case study for the 1999 Hector Mine earthquake demonstrates that the proposed stochastic-source-based method is a viable option in conducting probabilistic fault displacement hazard analysis. Full article
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<p>Probabilistic fault displacement hazard analysis based on stochastic source modelling.</p>
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<p>(<b>a</b>) Fault trace of the 1999 Hector Mine earthquake [<a href="#B22-geohazards-02-00022" class="html-bibr">22</a>] and GPS displacement vectors [<a href="#B20-geohazards-02-00022" class="html-bibr">20</a>,<a href="#B21-geohazards-02-00022" class="html-bibr">21</a>] and (<b>b</b>) displacement offsets along the fault trace [<a href="#B6-geohazards-02-00022" class="html-bibr">6</a>,<a href="#B22-geohazards-02-00022" class="html-bibr">22</a>].</p>
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<p>Existing inverted earthquake source models: (<b>a</b>) Ji et al. [<a href="#B23-geohazards-02-00022" class="html-bibr">23</a>], (<b>b</b>) Jonsson et al. [<a href="#B24-geohazards-02-00022" class="html-bibr">24</a>], and (<b>c</b>) Salichon et al. [<a href="#B25-geohazards-02-00022" class="html-bibr">25</a>]. Note that the source models for [<a href="#B24-geohazards-02-00022" class="html-bibr">24</a>,<a href="#B25-geohazards-02-00022" class="html-bibr">25</a>] were obtained from the SRCMOD database [<a href="#B26-geohazards-02-00022" class="html-bibr">26</a>].</p>
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<p>Model results based on Ji et al. [<a href="#B23-geohazards-02-00022" class="html-bibr">23</a>]: (<b>a</b>) source model, (<b>b</b>) displacements for three components (E-W, N-S, and U-D), (<b>c</b>) comparison of GPS displacement vectors with observations, (<b>d</b>) comparison of displacements at GPS stations with observations, and (<b>e</b>) comparison of horizontal and vertical offsets along the fault trace with observations. The numbers indicated in (<b>b</b>) correspond to the GPS station numbers that are indicated in (<b>d</b>).</p>
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<p>Variations of fault segment geometry: (<b>a</b>) dip = 75° with base strike angles, (<b>b</b>) dip = 80° with base strike angles, (<b>c</b>) dip = 85° with base strike angles, (<b>d</b>) dip = 80° with base strike angles minus 5°, and (<b>e</b>) dip = 80° with base strike angles plus 5°.</p>
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<p>Histogram of total scores of 1000 stochastic source models.</p>
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<p>Comparison of simulated displacements at GPS stations based on the top 24 stochastic source models with observations. The individual results of the top 24 models are shown in grey points, while the average GPS displacements of the top 24 models are shown with red squares.</p>
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<p>Comparison of simulated horizontal and vertical offsets along the fault trace based on the top 24 models with observations.</p>
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<p>Model results based on the stochastic source model 8 (total score = 32.37): (<b>a</b>) source model, (<b>b</b>) displacements for three components (E-W, N-S, and U-D), (<b>c</b>) comparison of GPS displacement vectors with observations, (<b>d</b>) comparison of displacements at GPS stations with observations, and (<b>e</b>) comparison of horizontal and vertical offsets along the fault trace with observations.</p>
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<p>Model results based on the stochastic source model 149 (total score = 31.81): (<b>a</b>) source model, (<b>b</b>) displacements for three components (E-W, N-S, and U-D), (<b>c</b>) comparison of GPS displacement vectors with observations, (<b>d</b>) comparison of displacements at GPS stations with observations, and (<b>e</b>) comparison of horizontal and vertical offsets along the fault trace with observations.</p>
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<p>Model results based on the stochastic source model 775 (total score = 31.68): (<b>a</b>) source model, (<b>b</b>) displacements for three components (E-W, N-S, and U-D), (<b>c</b>) comparison of GPS displacement vectors with observations, (<b>d</b>) comparison of displacements at GPS stations with observations, and (<b>e</b>) comparison of horizontal and vertical offsets along the fault trace with observations.</p>
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<p>Comparison of simulated total offsets along the fault trace based on the top 24 models with observations and predictions based on [<a href="#B6-geohazards-02-00022" class="html-bibr">6</a>] for the 1999 Hector Mine earthquake.</p>
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15 pages, 9319 KiB  
Article
The Relevance of Geotechnical-Unit Characterization for Landslide-Susceptibility Mapping with SHALSTAB
by Carla Moreira Melo, Masato Kobiyama, Gean Paulo Michel and Mariana Madruga de Brito
GeoHazards 2021, 2(4), 383-397; https://doi.org/10.3390/geohazards2040021 - 30 Nov 2021
Cited by 8 | Viewed by 3535
Abstract
Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the [...] Read more.
Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity. Full article
(This article belongs to the Collection Geohazard Characterization, Modeling, and Risk Assessment)
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<p>Location and altimetry of Jaguar creek basin, southern Brazil.</p>
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<p>Geotechnical map of the Jaguar creek basin and sampling points where the parameters ϕ, <span class="html-italic">c</span>, and <span class="html-italic">z</span> were estimated. R<sup>2</sup> is the coefficient of determination and <span class="html-italic">ρs</span> is the wet soil bulk density. The values presented here characterize the mechanical properties of soil near this slipping plane.</p>
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<p>Different scenarios elaborated for estimating the landslide susceptibility. Green boxes denote scenarios with discretized values per geotechnical unit, whereas blue boxes indicate scenarios in which constant values were adopted for the entire basin.</p>
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<p>Stability map of the Jaguar creek basin in Scenario 1.</p>
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<p>Stability map of the Jaguar stream basin (Scenario 4, sampling point 08).</p>
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<p>Stability map of the Jaguar creek basin with Scenario 13 (13 sample points and <span class="html-italic">z</span> = 3 m).</p>
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<p>ROC with the (<b>A</b>) threshold log <span class="html-italic">q</span>/<span class="html-italic">T</span> = −2.5. Scenario 1 presented the best performance; (<b>B</b>) threshold log <span class="html-italic">q</span>/<span class="html-italic">T</span> = −2.8. Scenario 4 presented the best performance; and (<b>C</b>) threshold log <span class="html-italic">q</span>/<span class="html-italic">T</span> = −3.1. Scenario 13 presented the best performance. The best scenarios are shown in the upper left corner of each graph.</p>
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<p>DPIUA results for the scenarios with the best performance: (<b>A</b>) Scenario 1, (<b>B</b>) Scenario 4, and(<b>C</b>) Scenario 13.</p>
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17 pages, 16209 KiB  
Article
Flash Flood Susceptibility Evaluation in Human-Affected Areas Using Geomorphological Methods—The Case of 9 August 2020, Euboea, Greece. A GIS-Based Approach
by Anna Karkani, Niki Evelpidou, Maria Tzouxanioti, Alexandros Petropoulos, Nicoletta Santangelo, Hampik Maroukian, Evangelos Spyrou and Lida Lakidi
GeoHazards 2021, 2(4), 366-382; https://doi.org/10.3390/geohazards2040020 - 19 Nov 2021
Cited by 12 | Viewed by 3857
Abstract
Flash floods occur almost exclusively in small basins, and they are common in small Mediterranean catchments. They pose one of the most common natural disasters, as well as one of the most devastating. Such was the case of the recent flood in Euboea [...] Read more.
Flash floods occur almost exclusively in small basins, and they are common in small Mediterranean catchments. They pose one of the most common natural disasters, as well as one of the most devastating. Such was the case of the recent flood in Euboea island, in Greece, in August 2020. A field survey was accomplished after the 2020 flash floods in order to record the main impacts of the event and identify the geomorphological and man-made causes. The flash flood susceptibility in the urbanized alluvial fans was further assessed using a Geographic Information System (GIS)-based approach. Our findings suggest that a large portion of the alluvial fans of Politika, Poros and Mantania streams are mainly characterized by high and very high hazard. In fact, ~27% of the alluvial fans of Politika and Poros streams are characterized with very high susceptibility, and ~54% of Psachna area. GIS results have been confirmed by field observations after the 2020 flash flood, with significant damages noted, such as debris flows and infrastructure damages, in buildings, bridges and the road networks. In addition, even though the adopted approach may be more time-consuming in comparison to purely computational methods, it has the potential of being more accurate as it combines field observations and the effect of past flooding events. Full article
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<p>Location of the study area. Our study focuses on Messapios river, and Politika, Poros and Mantania streams. Location of Lelas river is also indicated, as one of the areas impacted by the 2020 flash flood.</p>
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<p>Lithological map of the study area.</p>
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<p>Workflow illustrating the methodology adopted in this work.</p>
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<p>(<b>a</b>) The historical flood events and the affected areas. The yellow dot indicates the most recent flash flood events that took place near the town of Psachna. The sketching area indicates the general affected area by a historical flood events; (<b>b</b>) Map of the main geomorphological characteristics of the study area.</p>
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<p>(<b>a</b>) Slope map of the study area; (<b>b</b>) The road/hydraulic network of the study area.</p>
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<p>(<b>a</b>) Rainfall data from three stations near the study area for August of 2020 (data acquired from <a href="http://meteosearch.meteo.gr/" target="_blank">http://meteosearch.meteo.gr/</a> (accessed on 13 November 2021)); (<b>b</b>) Cumulative rainfall data from Steni station from 23:00 of 8 August 2020 to 10:00 of 9 August 2020 (based on [<a href="#B37-geohazards-02-00020" class="html-bibr">37</a>]).</p>
Full article ">Figure 6 Cont.
<p>(<b>a</b>) Rainfall data from three stations near the study area for August of 2020 (data acquired from <a href="http://meteosearch.meteo.gr/" target="_blank">http://meteosearch.meteo.gr/</a> (accessed on 13 November 2021)); (<b>b</b>) Cumulative rainfall data from Steni station from 23:00 of 8 August 2020 to 10:00 of 9 August 2020 (based on [<a href="#B37-geohazards-02-00020" class="html-bibr">37</a>]).</p>
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<p>Aerial photographs illustrating the damages caused by the 2020 flash flood. (<b>a</b>) Activation of an alluvial fan of Messapios River, which led to channel incision and deposition in the channel and on the fan surface. The red arrows show the direction of the flow. The yellow line shows the dispersion of fine-grained material in the marine environment after 20 days. (<b>b</b>,<b>c</b>) Damages within the settlement of Politika. (<b>d</b>) Damages at rural environment of Psachna.</p>
Full article ">Figure 7 Cont.
<p>Aerial photographs illustrating the damages caused by the 2020 flash flood. (<b>a</b>) Activation of an alluvial fan of Messapios River, which led to channel incision and deposition in the channel and on the fan surface. The red arrows show the direction of the flow. The yellow line shows the dispersion of fine-grained material in the marine environment after 20 days. (<b>b</b>,<b>c</b>) Damages within the settlement of Politika. (<b>d</b>) Damages at rural environment of Psachna.</p>
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<p>(<b>a</b>) Mudlines at 1.8 m, indicating the peak discharge of the flash flood (Politika stream—Politika); (<b>b</b>) seed lines (Politika stream—Drosia); (<b>c</b>) Impacts of debris floods on a bridge (Politika stream—Politika); (<b>d</b>) Impacts of debris floods on buildings (Politika stream—Politika).</p>
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<p>(<b>a</b>) Mudlines at 1.8 m, indicating the peak discharge of the flash flood (Politika stream—Politika); (<b>b</b>) seed lines (Politika stream—Drosia); (<b>c</b>) Impacts of debris floods on a bridge (Politika stream—Politika); (<b>d</b>) Impacts of debris floods on buildings (Politika stream—Politika).</p>
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<p>(<b>a</b>) The highway connecting Central and North Euboea, covered by landslide materials (Politika stream—Drosia); (<b>b</b>) A large boulder with a diameter of more than 2 m, carried away by the flow (Politika stream—Politika).</p>
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<p>Flash flood susceptibility calculated for the alluvial fans of Politika, Poros and Mantania streams.</p>
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14 pages, 1355 KiB  
Article
Public Institutional Structures for Disaster Preparedness in the Cereal Value Chain: A Zambian Case Study
by Brigadier Libanda
GeoHazards 2021, 2(4), 352-365; https://doi.org/10.3390/geohazards2040019 - 18 Nov 2021
Cited by 3 | Viewed by 2888
Abstract
Increasing extreme climate events and cyclonic activities provide clear evidence that the Southern African Development Community (SADC) region is a hotspot for climate change-driven natural disasters which critically disrupt agricultural production cycles. This is especially true with regard to the production of cereal, [...] Read more.
Increasing extreme climate events and cyclonic activities provide clear evidence that the Southern African Development Community (SADC) region is a hotspot for climate change-driven natural disasters which critically disrupt agricultural production cycles. This is especially true with regard to the production of cereal, produce widely used to represent food security. Although studies have attempted to disentangle the effect of demand vis à vis projected population growth on cereal production across the region, the contradiction between cereal production and climate disaster preparedness remains poorly resolved. Therefore, literature on the subject matter is scanty. The present study is motivated by the need to overcome this paucity of literature and thus, deepen our understanding of cereal production and climate disaster preparedness in the region. Therefore, the main aim of this study is to assess public institutional support structures that are currently being employed for climate disaster preparedness in the cereal value chain across Zambia as perceived by small scale farmers. After a comprehensive assessment of focus group discussions (FGDs), several points emerge specifically highlighting four salient findings: first, results show that a government-led Farmer Input Support Programme (FISP) is the only strategy particularly targeted at disaster preparedness. All other initiatives are targeted at improving or safeguarding livelihoods with some components having a ripple effect on the cereal value chain. Second, results show that climate forecasts that are supposed to trigger early action are generally characterized by low prediction skill with more false alarms and misses than hits. Third, forecasts were found to lack geographical specificity with generalities over large areas being common thus, diminishing their usefulness at the local scale. Fourth, end-users found forecasts to usually contain technical jargon that is difficult to decipher especially that most small-scale farmers are illiterate. This study concludes that to fully support the cereal value chain and realize food security in Zambia, policy formulation that champion the establishment of an effective early warning and early action system (EWEAS) involving multiple interest groups and actors should be considered a matter of urgency. Full article
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<p>Map of the study area in Africa shown in red on the insert. Asterisks on the map of Zambia indicate data collection points. Shading in the map of Zambia shows respective Agroecological Regions.</p>
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<p>Institutional support structure providing climate disaster preparedness as perceived by smallholder farmers. UN, United Nations; NGOs, Non-Governmental Organizations; GRZ, Government of the Republic of Zambia; ZMD, Zambia Meteorological Department; DMMU, Disaster Management and Mitigation Unit; MA, Ministry of Agriculture.</p>
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<p>Small scale farmer perception of the importance of climate information for disaster preparedness in the cereal value chain.</p>
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<p>Sources of climate information used by respondents for disaster preparedness in the cereal value chain.</p>
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20 pages, 5036 KiB  
Article
Dynamic Identification Tests of 20th Century Historic Masonry Buildings in Japan
by Yohei Endo, Yuta Waki, Yasushi Niitsu and Toshikazu Hanazato
GeoHazards 2021, 2(4), 332-351; https://doi.org/10.3390/geohazards2040018 - 31 Oct 2021
Cited by 1 | Viewed by 3220
Abstract
This paper discussed the application of health monitoring systems to 20th-century historic buildings. Natural disasters are major threats to monuments. They are often seismically vulnerable and require interventions. However, taking into account their historic and cultural values, it is appropriate to observe long-term [...] Read more.
This paper discussed the application of health monitoring systems to 20th-century historic buildings. Natural disasters are major threats to monuments. They are often seismically vulnerable and require interventions. However, taking into account their historic and cultural values, it is appropriate to observe long-term behaviour before making a decision on intervention schemes. To this aim, health monitoring is considered an effective approach. In recent years, MEMS (micro-electromechanical systems) accelerometers have been attracting attention for their convenience and efficacy. Nonetheless, the reliability of MEMS accelerometers still needs to be examined for the monitoring of monuments as sufficient research contributions have not been made. This paper presented two case studies that were monitored by means of MEMS accelerometers. They were masonry structures positioned in seismic-prone regions in Japan. A number of earthquakes were detected by the accelerometers during one year of monitoring. To examine the accuracy of the adopted MEMS accelerometers, dynamic identification tests were conducted using high-sensitivity strain-gauge accelerometers and servo velocity meters. Based on responses obtained from the tests, numerical simulation was performed. Nonlinear static analysis was performed. The numerical simulation permitted the comparison of reliability among sensors and test types. This paper provided suggestions for the dynamic identification tests of heritage structures. Full article
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<p>Sensors used in this study: (<b>a</b>) strain gauge accelerometer, (<b>b</b>) servo velocity meter and (<b>c</b>) MEMS accelerometer.</p>
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<p>Otaru warehouse complex: (<b>a</b>) south elevation, (<b>b</b>) the studied warehouse, (<b>c</b>) interior view, (<b>d</b>) site plan, (<b>e</b>) plan of the studied warehouse, (<b>f</b>) a–a’ section and (<b>g</b>) nogging coupling stud and column.</p>
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<p>Matsumoto storehouse: (<b>a</b>) west elevation; (<b>b</b>) north elevation; (<b>c</b>) plan and (<b>d</b>) a–a’ section.</p>
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<p>Occurrence of earthquakes during the monitoring period: (<b>a</b>) locations of the two earthquakes; acceleration time history of the 2021 Fukushima earthquake (<b>b</b>) and of the May 2021 Miyagi earthquake (<b>c</b>,<b>d</b>) response spectra ((<b>a</b>) originally from [<a href="#B65-geohazards-02-00018" class="html-bibr">65</a>], modified by the authors, (<b>b</b>–<b>d</b>) from [<a href="#B66-geohazards-02-00018" class="html-bibr">66</a>]).</p>
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<p>Vibration testing of the Otaru warehouse: (<b>a</b>) location of the velocity meters, (<b>b</b>) installation of velocity meters to a stud and wall, (<b>c</b>) manual excitation during forced vibration tests, the acceleration time history of VO2 sensor (<b>d</b>) and of VO5 sensor (<b>e</b>,<b>f</b>) Fourier spectrum of sensors and (<b>g</b>) wall/stud to floor frequency response functions.</p>
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<p>Vibration testing of the Matsumoto storehouse: (<b>a</b>) location of the accelerometers, (<b>b</b>) installation of an accelerometer, the acceleration time history of forced vibration test of sensor VM1 (<b>c</b>) and VM4 (<b>d</b>), Fourier spectrum of sensor VM1 (<b>e</b>) and of sensor VM4 (<b>f</b>), frequency response function of VM1/VM6 sensors (<b>g</b>) and of VM4/VM7 sensors (<b>h</b>).</p>
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<p>Monitoring of the Otaru warehouse: (<b>a</b>) Installed MEM sensor, acceleration time history of the HO1 sensor during the 2021 Fukushima earthquake (<b>b</b>) and during the May 2021 Miyagi earthquake (<b>c</b>), wall/stud to floor frequency response functions obtained from the 2021 Fukushima earthquake (<b>d</b>) and from the May 2021 Miyagi earthquake (<b>e</b>).</p>
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<p>Monitoring of the Matsumoto storehouse: (<b>a</b>) instillation of a MEMS sensor, (<b>b</b>) locations of sensors, the acceleration time history of the HM1 sensor (<b>c</b>) and the HM3 sensor (<b>d</b>), frequency response function of HM1/HM4 (<b>e</b>) and of HM3/HM4 (<b>f</b>).</p>
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<p>Numerical models: (<b>a</b>) the Otaru warehouse and (<b>b</b>) the Matsumoto storehouse.</p>
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<p>Mode shapes identified by eigenvalue analysis: (<b>a</b>) fundamental mode shape of the Otaru warehouse, fundamental mode shapes of the Matsumoto storehouse in the <span class="html-italic">x-</span>direction (<b>b</b>) and in the <span class="html-italic">y-</span>direction (<b>c</b>).</p>
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<p>Nonlinear static analysis of the Otaru warehouse: (<b>a</b>) principal positive strain distribution contours close to the failure of the structure and (<b>b</b>) relations between base acceleration and displacement.</p>
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<p>Nonlinear static analysis of the Matsumoto storehouse: principal positive strain distribution contours close to the failure of the structure in the <span class="html-italic">x-</span>direction (<b>a</b>) and in the <span class="html-italic">y-</span>direction (<b>b</b>), relations between base acceleration and displacement in the <span class="html-italic">x-</span>direction (<b>c</b>) and in the <span class="html-italic">y-</span>direction (<b>d</b>).</p>
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<p>Comparison of the mode shape between the experiment and numerical analysis: (<b>a</b>) Otaru warehouse, east wall, Matsumoto storehouse, north wall (<b>b</b>) and west wall (<b>c</b>).</p>
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11 pages, 2663 KiB  
Article
Assessing the Climatological Relationship between Heatstroke Risk and Heat Stress Indices in 47 Prefectures in Japan
by Yuki Iwamoto and Yukitaka Ohashi
GeoHazards 2021, 2(4), 321-331; https://doi.org/10.3390/geohazards2040017 - 29 Oct 2021
Cited by 9 | Viewed by 4786
Abstract
This study provides a decade-long link between summer heatstroke incidence and certain heat stress indices in 47 prefectures of Japan. The results for each prefecture were determined from the age-adjusted heatstroke incidence rate (TRadj) with heatstroke patients transported by ambulance, [...] Read more.
This study provides a decade-long link between summer heatstroke incidence and certain heat stress indices in 47 prefectures of Japan. The results for each prefecture were determined from the age-adjusted heatstroke incidence rate (TRadj) with heatstroke patients transported by ambulance, as well as from the daily maximum temperature (TEMPmax), maximum wet-bulb globe temperature (WBGTmax), and maximum universal thermal climate index (UTCImax) recorded from July to September of 2010–2019. The UTCImax relatively increased the vulnerability in many prefectures of northern Japan more distinctly than the other indices. In the following analysis, the ratio of the TRadj of the hottest to coolest months using the UTCImax was defined as the heatstroke risk of the hottest to coolest (HRHC). Overall, the HRHC varied approximately from 20 to 40 in many prefectures in the past decade. In contrast, for the same analysis performed in each month, HRHC ratios in July and August fell within 2–4 in many prefectures, whereas in September, the average and maximum HRHC ratios for all prefectures were 7.0 and 32.4, respectively. This difference can be related to the large difference in UTCImax between the maximum and minimum for a decade. Full article
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<p>The 47-prefecture map of Japan Island. Eight regions shown here are colour-coded. Prefectural names are listed in <a href="#app1-geohazards-02-00017" class="html-app">Tables S1–S7</a> with the prefectural number shown in this figure.</p>
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<p>Preview maps of (<b>a</b>) <span class="html-italic">TR<sub>adj</sub></span>, (<b>b</b>) <span class="html-italic">TEMP<sub>max</sub></span>, (<b>c</b>) <span class="html-italic">WBGT<sub>max</sub></span>, and (<b>d</b>) <span class="html-italic">UTCI<sub>max</sub></span> aggregated for the hot season from July to September in 2010–2019. The <span class="html-italic">TR<sub>adj</sub></span> averaged the accumulated value for three months per year, while the <span class="html-italic">TEMP<sub>max</sub></span>, <span class="html-italic">WBGT<sub>max</sub></span>, and <span class="html-italic">UTCI<sub>max</sub></span> averaged the mean value for the three months per year.</p>
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<p>Relationship between <span class="html-italic">TR<sub>adj</sub></span> and (<b>a</b>) <span class="html-italic">TEMP<sub>max</sub></span>, (<b>b</b>) <span class="html-italic">WBGT<sub>max</sub></span>, and (<b>c</b>) <span class="html-italic">UTCI<sub>max</sub></span> in the 47 pre <a href="#geohazards-02-00017-f002" class="html-fig">Figure 2</a>. The dashed line indicates a third-order regression curve with a coefficient of determination (R<sup>2</sup>). Colours in the mark correspond to those of the region shown in <a href="#geohazards-02-00017-f001" class="html-fig">Figure 1</a>.</p>
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<p>Relationship between <span class="html-italic">TR<sub>adj</sub></span> and (<b>a</b>) <span class="html-italic">TEMP<sub>max</sub></span> and (<b>b</b>) <span class="html-italic">UTCI<sub>max</sub></span> for the hot season from July to September in 2010–2019. Seven prefectures here have cities with million population in each region. The <span class="html-italic">TR<sub>adj</sub></span> is a monthly value for each year, while the <span class="html-italic">TEMP<sub>max</sub></span>, <span class="html-italic">WBGT<sub>max</sub></span>, and <span class="html-italic">UTCI<sub>max</sub></span> are the monthly averaged value for each month in each year. <span class="html-italic">TR<sub>adj</sub></span> is represented as a logarithmic axis. The solid line indicates a regression line with a coefficient of determination (R<sup>2</sup>).</p>
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<p>47 prefecture maps of constant <span class="html-italic">b</span> (vulnerability) in Equation (2), obtained from using (<b>a</b>) <span class="html-italic">TEMP<sub>max</sub></span>, (<b>b</b>) <span class="html-italic">WBGT<sub>max</sub></span>, and (<b>c</b>) <span class="html-italic">UTCI<sub>max</sub></span>, with result for the hot season from July to September in 2010–2019. The maximum, average, and minimum <span class="html-italic">b</span> values of the 47 prefectures are also listed in the bottom of map.</p>
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<p>Respective <span class="html-italic">TR<sub>adj</sub></span> (bars) in the maximum and minimum months of <span class="html-italic">UTCI<sub>max</sub></span> and its ratio (HRHC; line with circles) of the maximum to minimum in the 47 prefectures, with result for the hot season from July to September in 2010–2019.</p>
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<p>Monthly ratio in <span class="html-italic">TR<sub>adj</sub></span> of the maximum <span class="html-italic">UTCI<sub>max</sub></span> to the minimum (HRHC), which is divided by each month for <a href="#geohazards-02-00017-f006" class="html-fig">Figure 6</a>, with result for 2010–2019 in the 47 prefectures. The maximum, average, and minimum HRHC of the 47 prefectures are also listed in the upper portion of the graph.</p>
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<p>Relationship between the monthly ratio in <span class="html-italic">TR<sub>adj</sub></span> of the maximum <span class="html-italic">UTCI<sub>max</sub></span> to the minimum (HRHC) and the monthly difference in <span class="html-italic">UTCI<sub>max</sub></span> between the maximum and minimum in the decade. The 25, 50, and 75 percentiles (numerals) for results in the 47 prefectures are depicted for each month.</p>
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19 pages, 3598 KiB  
Article
Threshold Effects of Relative Sea-Level Change in Intertidal Ecosystems: Empirical Evidence from Earthquake-Induced Uplift on a Rocky Coast
by Shane Orchard, Hallie S. Fischman, Shawn Gerrity, Tommaso Alestra, Robyn Dunmore and David R. Schiel
GeoHazards 2021, 2(4), 302-320; https://doi.org/10.3390/geohazards2040016 - 29 Sep 2021
Cited by 13 | Viewed by 3319
Abstract
Widespread mortality of intertidal biota was observed following the 7.8 Mw Kaikōura earthquake in November 2016. To understand drivers of change and recovery in nearshore ecosystems, we quantified the variation in relative sea-level changes caused by tectonic uplift and evaluated their relationships with [...] Read more.
Widespread mortality of intertidal biota was observed following the 7.8 Mw Kaikōura earthquake in November 2016. To understand drivers of change and recovery in nearshore ecosystems, we quantified the variation in relative sea-level changes caused by tectonic uplift and evaluated their relationships with ecological impacts with a view to establishing the minimum threshold and overall extent of the major effects on rocky shores. Vertical displacement of contiguous 50 m shoreline sections was assessed using comparable LiDAR data to address initial and potential ongoing change across a 100 km study area. Co-seismic uplift accounted for the majority of relative sea-level change at most locations. Only small changes were detected beyond the initial earthquake event, but they included the weathering of reef platforms and accumulation of mobile gravels that continue to shape the coast. Intertidal vegetation losses were evident in equivalent intertidal zones at all uplifted sites despite considerable variation in the vertical displacement they experienced. Nine of ten uplifted sites suffered severe (>80%) loss in habitat-forming algae and included the lowest uplift values (0.6 m). These results show a functional threshold of c.1/4 of the tidal range above which major impacts were sustained. Evidently, compensatory recovery has not occurred—but more notably, previously subtidal algae that were uplifted into the low intertidal zone where they ought to persist (but did not) suggests additional post-disturbance adversities that have contributed to the overall effect. Continuing research will investigate differences in recovery trajectories across the affected area to identify factors and processes that will lead to the regeneration of ecosystems and resources. Full article
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<p>Location of the study area on the east coast of the South Island of New Zealand.</p>
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<p>(<b>A</b>) Sampling setup for differencing analysis of 1 × 1 m digital elevation models derived from LiDAR data showing sampling origin points at 50 m spacing on an assessment baseline located at the approximate position of the pre-earthquake mean high water springs. 50 × 50 m analysis windows landward of this line are within the spatial extent of all LiDAR datasets. Shore-perpendicular transects extending seaward were used to associate each analysis window with the dominant substrate type in the adjacent intertidal area. Two of the field survey sites (Waipapa North and Waipapa Lagoon) are located in the inset. (<b>B</b>) Manually constrained analysis windows used to assess uplift at the Waipapa sites where block-faulting uplifted intertidal areas higher than was recorded in the analysis windows to landward on the assessment baseline. The underlying image is a difference model with the same uplift scale as (<b>A</b>) for the pre-quake—immediate post-quake period.</p>
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<p>Workflow for differencing analyses. (<b>A</b>) Aerial image captured concurrent with LiDAR data showing examples of surface deformation features associated with the earthquake. The acquisition date was November 2016 (immediate post-quake). (<b>B</b>) 1 × 1 m digital elevation model constructed from LiDAR data at the same date. (<b>C</b>) Example of slope mask used to constrain the analysis domain to slopes &lt;5 degrees. (<b>D</b>) Example of differencing result for July 2012 and December 2016 ground heights, with the former subtracted from the latter.</p>
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<p>Regressions of uplift values obtained from all combinations of the three analysis window sizes (50 × 50 m, 50 × 200 m, 50 × 500 m) that differ in their landward dimension perpendicular to the coastline.</p>
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<p>Vertical displacement of the Kaikōura coast calculated for two time periods (<b>A</b>) 2012–2016 and (<b>B</b>) 2012–2018 using independent differencing analyses and a 5-degree slope constraint to control for horizontal displacement effects. The LiDAR datasets have comparable resolution but slightly different coverage. The gap in coverage at c. 35 km on the X axis is the Kaikōura Peninsula. This area was outside of the LiDAR acquisition extent in the immediate post-quake dataset (December 2016), but was included in the June 2018 acquisition, enabling the analysis of 2012 to 2018 ground-level changes for the entire study area. (<b>C</b>) Estimated vertical displacement of the entire coastline between July 2012 and June 2018. Vertical displacements recorded in national geodetic updates to the Land Information New Zealand (LINZ) survey benchmark network to November 2018 are also shown for comparison.</p>
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<p>Histograms of the degree of uplift experienced within a classification of four substrate types found on the Kaikōura coast for the period July 2012–June 2018. Calculations used a 50 × 50 m analysis window and a 5-degree slope constraint to control for horizontal displacement effects.</p>
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<p>Summary of earthquake-induced mortality by major intertidal taxa and associated bare ground changes for 12 sites that experienced various degrees of uplift on the Kaikōura coast. Colours represent the severity of changes in percentage cover from pre-earthquake values as measured in post-earthquake field surveys within the equivalent intertidal zone with the highest severity recorded over three surveys presented here.</p>
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<p>Impacts of uplift on the Kaikōura coast. (<b>A</b>) Waipapa Lagoon site on high tide in the post-quake landscape. Dead and dying bull kelp (<span class="html-italic">Durvillaea</span> spp.) and other brown seaweeds can be seen. The pre-quake high tide mark is at the top of the large rocks, a displacement of nearly 5 m. (<b>B</b>) Reef erosion at Wairepo six months after the earthquake. The washer was flush with the rock surface when installed immediately post-quake. (<b>C</b>,<b>D</b>) A graphic illustration of the severity of impacts on habitat-forming algae at Wairepo. Despite experiencing only modest uplift, nearly all seaweeds including <span class="html-italic">Hormosira</span> (in foreground) perished soon after the earthquake.</p>
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