GeoHazards, Volume 4, Issue 2 (June 2023) – 6 articles

With the increasing availability and diversity of satellite imagery, the multisensor fusion of data can more effectively address the improved monitoring of temporary water bodies. This study supports the attempt to apply well established methods to detect spatial and temporal changes in ephemeral shallow lakes in lowland karst terrain, as well as to improve the understanding concerning the dynamics of water storage and hydrological mechanisms during extreme precipitation events. Based on the joint analysis of Copernicus Sentinel SAR and optical mission data, as well as soil moisture and EO-based rainfall observations over the period of 2015–2020, we demonstrated the control of the karst system on the ephemeral lake appearances in the broader area of Chalkida (Evvia, Greece). A connection between the prolonged and extended water coverage in the ephemeral lakes and flooding in the area is documented. Our EO-supported findings may serve as indicators for flood alerts in future extreme precipitation events, improving responses in cases of emergencies. Full article

The Jacotines landslide is representative of the large mass movements that affect the Champagne vineyards. Understanding the subsurface structure of these slopes and the mechanisms leading to sliding events is of a great interest, particularly for winegrowers who produce Champagne. This knowledge is generally used to elaborate accurate hazard assessment maps, which is an important feature in land use planning. The approach presented is based on the integration of geophysical imaging (seismic wave velocity and electrical resistivity), lithostratigraphic analysis (drilling core) and geomorphological investigations (surface landforms) to reconstruct the relations between the landslide structure, surface water flow, groundwater regime and the overall slope stability. A first phase of instability resulting in a large rotational slip probably occurred during the Late Glacial Period in morphoclimatic conditions characterized by an excess of water. A second one, still active, leading to superficial reactivations and relates to present hydrogeological conditions determined by the internal structure of the landslide. Full article

In the mapping and assessment of mountain hazard susceptibility using machine learning models, the selection of model parameters plays a critical role in the accuracy of predicting models. In this study, we present a novel approach for developing a prediction model based on random forest (RF) by incorporating ensembles of hyperparameter optimization. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, which is a typical hazard-prone, mountainous area. We identified fourteen influential factors based on field measurements to describe the “avalanche–landslide–debris flow” hazard chains in the study area. We constructed training (80%) and validation (20%) datasets for 378 hazard sites. The performance of the models was assessed using standard statistical metrics, including recall, confusion matrix, accuracy, F1, precision, and area under the operating characteristic curve (AUC), based on a multicollinearity analysis and Relief-F two-step evaluation. The results indicate that all three models, i.e., RF, GA-RF, and Bayes-RF, achieved good performance (AUC: 0.89~0.92). The Bayes-RF model outperformed the other two models (AUC = 0.92). Therefore, this model is highly accurate and robust for mountain hazard susceptibility assessment and is useful for the study area as well as other regions. Additionally, stakeholders can use the susceptibility map produced to guide mountain hazard prevention and control measures in the region. Full article

The computational simulation of rivers is a useful tool that can be applied in a wide range of situations from providing real time alerts to the design of future mitigation plans. However, for all the applications, there are two important requirements when modeling river behavior: accuracy and reasonable computational times. This target has led to recent developments in numerical models based on the full two-dimensional (2D) shallow water equations (SWE). This work presents a GPU accelerated 2D SW model for the simulation of flood events in real time. It is based on a well-balanced explicit first-order finite volume scheme able to run over dry beds without the numerical instabilities that are likely to occur when used in complex topography. The model is applied to reproduce a real event in the reach of the Ebro River (Spain) with a downstream reservoir, in which a study of the most appropriate boundary condition (BC) for modeling of the dam is assessed (time-dependent level condition and weir condition). The whole creation of the model is detailed in terms of mesh optimization and validation. The simulation results are compared with field data over the flood duration (up to 20 days), allowing an analysis of the performance and time saved by different GPU devices and with the different BCs. The high values of fit between observed and simulated results, as well as the computational times achieved, are encouraging to propose the use of the model as a forecasting system. Full article

Offshore basalts, most commonly found as oceanic crust formed at mid-ocean ridges, are estimated to offer an almost unlimited reservoir for CO₂ sequestration and are regarded as one of the most durable locations for carbon sequestration since injected CO₂ will mineralize, forming carbonate rock. As part of the Solid Carbon project, the potential of the Cascadia Basin, about 200 km off the west coast of Vancouver Island, Canada, is investigated as a site for geological CO₂ sequestration. In anticipation of a demonstration proposed to take place, it is essential to assess the tendency of geologic faults in the area to slip in the presence of CO₂ injection, potentially causing seismic events. To understand the viability of the reservoir, a quantitative risk assessment of the proposed site area was conducted. This involved a detailed characterization of the proposed injection site to understand baseline stress and pressure conditions and identify individual faults or fault zones with the potential to slip and thereby generate seismicity. The results indicate that fault slip potential is minimal (less than 1%) for a constant injection of up to ~2.5 MT/yr. This is in part due to the thickness of the basalt aquifer and its permeability. The results provide a reference for assessing the potential earthquake risk from CO₂ injection in similar ocean basalt basins. Full article

The final goal of this paper is to contribute to the difficult task of understanding and forecasting submarine volcanic eruption activity by proposing a method to quantify discolored water. To achieve this purpose, we quantitatively analyzed the discolored seawater seen before and after the eruption of the marine environment around the Indonesian submarine volcano “Anak Krakatau”, which erupted at the end of December 2018, from the viewpoint of the “dominant wavelength”. The atmospherically corrected COM-C SGLI data for 17 periods from the eruption from October 2018 to March 2019 were used. As a result, the following three main items were found. First, the average ± standard deviation of the entire dominant wavelength was 497 nm ± 2 nm before the eruption and 515 nm ± 35 nm after the eruption. Second, the discolored water area around the island derived from SGLI was detected from the contour line with dominant wavelengths of 500 nm and 560 nm. Third, the size of a dominant wavelength of 500 nm or more in the discolored water areas changed in a complicated manner within the range of almost 0 to 35 km². The area of the dominant wavelength of 500 nm or more slightly increased just before the eruption. Finally, it was proven that the “dominant wavelength” from the SGLI proposed in this paper can be a very effective tool in understanding or predicting submarine volcanic activity. Full article